Human vision is an important factor in the areas of image processing. Research has been done for years to make automatic image processing but still human intervention can not be denied and thus better human intervention is necessary. Two most important points are required to improve human vision which are light and color. Gamma encoder is the one which helps to improve the properties of human vision and thus to maintain visual quality gamma encoding is necessary.
It is to mention that all through the computer graphics RGB (Red, Green, and Blue) color space is vastly used. Moreover, for computer graphics RGB color space is called the most established choice to acquire desired color. RGB color space has a great effort on simplifying the design and architecture of a system. However, RGB struggles to deal efficiently for the images those belong to the real-world.
Images are captured using cameras, videos and other devices using different magnifications. In most cases during processing, in compare to the original outlook the images appear either dark or bright in contrast. Human vision affects and thus poor quality image analysis may occur. Consequently this poor manual image analysis may have huge difference from the computational image analysis outcome. Question may arise here why we will use gamma encoding when histogram equalization or histogram normalization can enhance images. Enhancing images does not improve human visualization quality all the time because sometimes it brightens the image quality when it is needed to darken and vice-versa. Human vision reflects under universal illumination environment (not pitch black or blindingly bright) thus follows an approximate gamma or power function. Hence, this is not a good idea to brighten images all the time when better human visualization can be obtained while darkening the images. Better human visualization is important for manual image processing which leads to compare the outcome with the semiautomated or automated one. Considering the importance of gamma encoding in image processing we propose an efficient color model which will help to improve visual quality for manual processing as well as will lead analyzers to analyze images automatically for comparison and testing purpose.
This document discusses image enhancement techniques in digital image processing. It defines image enhancement as modifying image attributes to make an image more suitable for a given task. The main techniques discussed are spatial domain enhancement methods like noise removal, contrast adjustment, and histogram equalization. Examples are provided to demonstrate the effects of these enhancement methods on images.
This document provides an overview of image enhancement techniques. It discusses the objectives of image enhancement, which is to process an image to make it more suitable for a specific application or task. The document focuses on spatial domain techniques for image enhancement, specifically point processing methods and histogram processing. It categorizes image enhancement methods into two broad categories: spatial domain methods, which directly manipulate pixel values; and frequency domain methods, which first convert the image into the frequency domain before performing enhancements.
Review on Image Enhancement in Spatial Domainidescitation
With the proliferation in electronic imaging devices
like in mobiles, computer vision, medical field and space field;
image enhancement field has become the quite interesting
and important area of research. These imaging devices are
viewed under a diverse range of viewing conditions and a huge
loss in contrast under bright outdoor viewing conditions; thus
viewing condition parameters such as surround effects,
correlated color temperature and ambient lighting have
become of significant importance. Therefore, Principle
objective of Image enhancement is to adjust the quality of an
image for better human visual perception. Appropriate choice
of enhancement techniques is greatly influenced by the
imaging modality, task at hand and viewing conditions.
Basically, image enhancement techniques have been classified
into two broad categories: Spatial domain image enhancement
and Frequency domain image enhancement. This survey report
gives an overview of different methodologies have been used
for enhancement under the spatial domain category. It is noted
that in this field still more research is to be done.
The students can learn about basics of image processing using matlab.
It explains the image operations with the help of examples and Matlab codes.
Students can fine sample images and .m code from the link given in slides.
Abstract
Field of image processing has vast applications in medical, forensic, research etc., It includes various domains like enhancement,
classification, segmentation, etc., which are widely used for these applications. Image Enhancement is the pre processing step on
which the accuracy of the result lies. Image enhancement aims to improve the visual appearance of an image, without affecting
the original attributes (i.e.,) image contrast is adjusted and noise is removed to produce better quality image. Hence image
enhancement is one of the most important tasks in image processing. Enhancement is classified into two categories spatial domain
enhancement and frequency domain enhancement. Spatial domain enhancement acts upon pixel value whereas frequency domain
enhancement acts on the Fourier transform of the image. The enhancement techniques to be used depend on modality, climatic
and visual perspective etc., In this paper, we present a survey on various existing image enhancement techniques.
Keywords: Enhancement, Spatial domain enhancement, Frequency domain enhancement, Contrast, Modality.
The document discusses various image enhancement techniques in Matlab, including filtering, predefined filters, image enhancement tools, image restoration, dilation/erosion functions, and dithering. Filtering can be used to modify images through operations like smoothing, sharpening, and edge enhancement. Predefined filters like 'gaussian' and 'laplacian' can be applied to images with functions like fspecial and imfilter. Additional tools for operations such as histogram equalization, noise addition, and filtering are also covered.
The document summarizes an adaptive compressed image hashing method using modified Center Symmetric Local Binary Pattern (CSLBP) and color weight factors derived from the L*a*b* color space. The proposed method extracts texture features using modified CSLBP on the luminance channel of the L*a*b* color space. Color weight factors are then used adaptively, applying averaging for smooth regions and differencing for non-smooth regions, to enhance the discrimination capability of the generated hash. Experimental results using benchmarks like normalized hamming distance and ROC characteristics demonstrate the method can successfully differentiate between content-changing and content-preserving modifications of color images.
Digital Image Processing_ ch2 enhancement spatial-domainMalik obeisat
The document discusses image enhancement techniques in the spatial domain. It describes how image enhancement aims to process an image to make it more suitable for display or analysis by sharpening, smoothing, or normalizing illumination. Enhancement can be done as preprocessing or postprocessing. Common approaches include linear and non-linear operators that manipulate pixel values. Specific techniques covered include histogram equalization, thresholding, gamma correction, and filtering to modify contrast and brightness. The goal of histogram manipulation is to design transforms that modify an image histogram to have desired properties like increased contrast or matched to a reference histogram.
This document discusses image enhancement techniques in digital image processing. It defines image enhancement as modifying image attributes to make an image more suitable for a given task. The main techniques discussed are spatial domain enhancement methods like noise removal, contrast adjustment, and histogram equalization. Examples are provided to demonstrate the effects of these enhancement methods on images.
This document provides an overview of image enhancement techniques. It discusses the objectives of image enhancement, which is to process an image to make it more suitable for a specific application or task. The document focuses on spatial domain techniques for image enhancement, specifically point processing methods and histogram processing. It categorizes image enhancement methods into two broad categories: spatial domain methods, which directly manipulate pixel values; and frequency domain methods, which first convert the image into the frequency domain before performing enhancements.
Review on Image Enhancement in Spatial Domainidescitation
With the proliferation in electronic imaging devices
like in mobiles, computer vision, medical field and space field;
image enhancement field has become the quite interesting
and important area of research. These imaging devices are
viewed under a diverse range of viewing conditions and a huge
loss in contrast under bright outdoor viewing conditions; thus
viewing condition parameters such as surround effects,
correlated color temperature and ambient lighting have
become of significant importance. Therefore, Principle
objective of Image enhancement is to adjust the quality of an
image for better human visual perception. Appropriate choice
of enhancement techniques is greatly influenced by the
imaging modality, task at hand and viewing conditions.
Basically, image enhancement techniques have been classified
into two broad categories: Spatial domain image enhancement
and Frequency domain image enhancement. This survey report
gives an overview of different methodologies have been used
for enhancement under the spatial domain category. It is noted
that in this field still more research is to be done.
The students can learn about basics of image processing using matlab.
It explains the image operations with the help of examples and Matlab codes.
Students can fine sample images and .m code from the link given in slides.
Abstract
Field of image processing has vast applications in medical, forensic, research etc., It includes various domains like enhancement,
classification, segmentation, etc., which are widely used for these applications. Image Enhancement is the pre processing step on
which the accuracy of the result lies. Image enhancement aims to improve the visual appearance of an image, without affecting
the original attributes (i.e.,) image contrast is adjusted and noise is removed to produce better quality image. Hence image
enhancement is one of the most important tasks in image processing. Enhancement is classified into two categories spatial domain
enhancement and frequency domain enhancement. Spatial domain enhancement acts upon pixel value whereas frequency domain
enhancement acts on the Fourier transform of the image. The enhancement techniques to be used depend on modality, climatic
and visual perspective etc., In this paper, we present a survey on various existing image enhancement techniques.
Keywords: Enhancement, Spatial domain enhancement, Frequency domain enhancement, Contrast, Modality.
The document discusses various image enhancement techniques in Matlab, including filtering, predefined filters, image enhancement tools, image restoration, dilation/erosion functions, and dithering. Filtering can be used to modify images through operations like smoothing, sharpening, and edge enhancement. Predefined filters like 'gaussian' and 'laplacian' can be applied to images with functions like fspecial and imfilter. Additional tools for operations such as histogram equalization, noise addition, and filtering are also covered.
The document summarizes an adaptive compressed image hashing method using modified Center Symmetric Local Binary Pattern (CSLBP) and color weight factors derived from the L*a*b* color space. The proposed method extracts texture features using modified CSLBP on the luminance channel of the L*a*b* color space. Color weight factors are then used adaptively, applying averaging for smooth regions and differencing for non-smooth regions, to enhance the discrimination capability of the generated hash. Experimental results using benchmarks like normalized hamming distance and ROC characteristics demonstrate the method can successfully differentiate between content-changing and content-preserving modifications of color images.
Digital Image Processing_ ch2 enhancement spatial-domainMalik obeisat
The document discusses image enhancement techniques in the spatial domain. It describes how image enhancement aims to process an image to make it more suitable for display or analysis by sharpening, smoothing, or normalizing illumination. Enhancement can be done as preprocessing or postprocessing. Common approaches include linear and non-linear operators that manipulate pixel values. Specific techniques covered include histogram equalization, thresholding, gamma correction, and filtering to modify contrast and brightness. The goal of histogram manipulation is to design transforms that modify an image histogram to have desired properties like increased contrast or matched to a reference histogram.
Efficient Image Compression Technique using JPEG2000 with Adaptive ThresholdCSCJournals
Image compression is a technique to reduce the size of image which is helpful for transforms. Due to the limited communication bandwidth we have to need optimum compressed image with good visual quality. Although the JPEG2000 compression technique is ideal for image processing as it uses DWT (Discrete Wavelet Transform).But in this paper we proposed fast and efficient image compression scheme using JPEG2000 technique with adaptive subband threshold. Actually we used subband adaptive threshold in decomposition section which gives us more compression ratio and good visual quality other than existing compression techniques. The subband adaptive threshold that concentrates on denoising each subband (except lowest coefficient subbands) by minimizing insignificant coefficients and adapt with modified coefficients which are significant and more responsible for image reconstruction. Finally we use embedded block coding with optimized truncation (EBCOT) entropy coder that gives three different passes which gives more compressed image. This proposed method is compared to other existing approach and give superior result that satisfy the human visual quality and also these resulting compressed images are evaluated by the performance parameter PSNR.
Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis. For example, you can remove noise, sharpen, or brighten an image, making it easier to identify key features.
Here are some useful examples and methods of image enhancement:
Filtering with morphological operators, Histogram equalization, Noise removal using a Wiener filter, Linear contrast adjustment, Median filtering, Unsharp mask filtering, Contrast-limited adaptive histogram equalization (CLAHE). Decorrelation stretch
CONTRAST ENHANCEMENT AND BRIGHTNESS PRESERVATION USING MULTIDECOMPOSITION HIS...sipij
Histogram Equalization (HE) has been an essential addition to the Image Enhancement world.
Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization
(AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE)
methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final
image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization
(MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input
image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in
correct order. The final image after MDHE gives us the best results based on contrast enhancement and
brightness preservation aspect compared to all other techniques mentioned above. We have calculated the
various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported
by bar graphs, histograms and the parameter calculations at the end.
This document discusses various methods for contrast enhancement of images, including:
- Local color correction, which enhances contrast locally rather than globally.
- Simplest color balance, which clips a percentage of dark and light pixels before normalization.
- Screened Poisson equation, which acts as a high-pass filter using a single contrast parameter. Implementations of these methods in various color spaces like RGB, HSI, HSV, and HSL are provided. Local color correction is shown to perform better than global gamma correction by handling both dark and bright areas simultaneously.
This document discusses image segmentation techniques. It describes how segmentation partitions an image into meaningful regions based on discontinuities or similarities in pixel intensity. The key methods covered are thresholding, edge detection using gradient and Laplacian operators, and the Hough transform for global line detection. Adaptive thresholding is also introduced as a technique to handle uneven illumination.
Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retriev...IJERA Editor
This document evaluates the performance of the Euclidean and Manhattan distance metrics in a content-based image retrieval system. It finds that the Manhattan distance metric showed better precision than the Euclidean distance metric. The system uses color histograms and Gabor texture features to represent images. Color is represented in HSV color space and histograms of hue, saturation and value are used. Gabor filters are applied to capture texture at different scales and orientations. Distance between feature vectors is calculated using Euclidean and Manhattan distance formulas to find similar images from the database. The system was tested on a dataset of 1000 Corel images and Manhattan distance produced more relevant search results.
This document discusses digital image processing. It defines digital images as two-dimensional representations of values stored as pixels in computer memory. Digital image processing involves enhancing images, extracting information and features, and manipulating images using computer software. The document outlines common image processing techniques like image compression, enhancement, and measurement extraction. It also describes the basics of digital image editing using software to alter pixel values and change image properties.
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Image enhancement techniques can be divided into spatial and frequency domain methods. Spatial domain methods operate directly on pixel values using techniques like basic gray level transformations, contrast stretching and thresholding. These manipulations are used to accentuate image features, improve display quality or aid machine analysis by modifying pixel intensities within an image.
This document discusses various intensity transformation and spatial filtering techniques for digital image enhancement. It covers single pixel operations like negative image and contrast stretching. It also discusses neighborhood operations such as averaging and median filters. Finally, it discusses geometric spatial transformations like scaling, rotation and translation. The document provides details on basic intensity transformation functions including log, power law, and piecewise linear transformations. It also covers histogram processing techniques like histogram equalization, matching and local histogram processing. Spatial filtering and its mechanics are explained.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Histogram equalization (HE) is a simple and widely used image contrast enhancement technique. The basic disadvantage of HE is it changes the brightness of the image. In order to overcome this drawback, various HE methods have been proposed. These methods preserves the brightness on the output image but, does not have a natural look. In order to overcome this problem the, present paper uses Multi-HE methods, which decompose the image into several sub images, and classical HE method is applied to each sub image. The algorithm is applied on various images and has been analysed using both objective and subjective assessment.
This document compares image enhancement and analysis techniques using image processing and wavelet techniques on thermal images. It discusses various image enhancement methods such as converting images to grayscale, histogram equalization, contrast enhancement, linear and adaptive filtering, morphology, FFT transforms, and wavelet-based techniques including image fusion, denoising, and compression. Results showing enhanced, denoised, and compressed images are presented and analyzed. The document concludes that wavelet techniques provide better enhancement of thermal images compared to traditional image processing methods.
1) The document proposes a gradient-based method for low-light image enhancement. It extracts gradients from the input image, manipulates the gradients by applying higher gain to darker regions, and integrates the gradients while constraining the intensity range.
2) Experimental results show that the proposed method enhances low-light images effectively while avoiding saturation, compared to other techniques like histogram equalization.
3) The method runs in real-time and MATLAB code is available online for researchers.
This document discusses techniques for enhancing and analyzing thermal images using digital image processing. It begins with an overview of image enhancement, including highlighting details, removing noise, and increasing contrast. Thermal image enhancement is then discussed for applications in various fields. Key techniques covered include converting images to grayscale, histogram equalization, linear filtering for noise removal, morphology operations like erosion and dilation, and using fast Fourier transforms. A flowchart is proposed showing the sequence of applying these techniques to enhance an image.
This document provides an overview of various image enhancement techniques. It begins with an introduction to image enhancement and its objectives. It then outlines and describes several categories of enhancement methods, including spatial-frequency domain methods, point operations, histogram operations, spatial operations, and transform operations. Specific techniques discussed in detail include contrast stretching, clipping, thresholding, median filtering, unsharp masking, and principal component analysis for multispectral images. The document also covers color image enhancement and techniques for pseudocoloring.
The document discusses various image enhancement techniques in digital image processing. It describes point operations like image negative, contrast stretching, thresholding, brightness enhancement, log transformation, and power law transformation. Contrast stretching expands the range of intensity levels and can be done by multiplying pixels with a constant, using a transfer function, or histogram equalization. Thresholding converts an image to binary by assigning pixel values above a threshold to one level and below to another. Log and power law transformations compress high intensity values and expand low values to enhance an image. Matlab code examples are provided for each technique.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
At the end of this lesson, you should be able to;
identify color formation and how color visualize.
describe primary and secondary colors.
describe display on CRT and LCD.
comprehend RGB, CMY, CMYK and HSI color models.
Digital images can be enhanced in various ways to improve quality. There are three main categories of enhancement techniques: spatial domain, frequency domain, and combination methods. Spatial domain methods operate directly on pixel values using point processing or neighborhood filtering. Key spatial techniques include contrast stretching, thresholding, and histogram equalization. Frequency domain methods modify an image's Fourier transform. Common transformations include logarithmic, power-law, and piecewise linear functions, which can increase contrast or highlight certain grayscale ranges. Proper enhancement improves an image's features for desired applications.
A Comparative Study on Image Contrast Enhancement TechniquesIRJET Journal
This document presents a comparative study of various image contrast enhancement techniques. It discusses techniques like histogram equalization, gamma correction, brightness preserving bi-histogram equalization (BBHE), brightness preserving dynamic histogram equalization (BPDHE), and region based adaptive contrast enhancement (RACE). The study evaluates the performance of these techniques on different color images using objective parameters like entropy, absolute contrast error, and peak signal to noise ratio. The results show that the BPDHE technique generally produces enhanced images with less color error, higher contrast-to-noise ratio, and entropy values indicating more details compared to the other techniques. BPDHE is therefore found to be the best technique for enhancing image contrast while preserving color and brightness.
This document summarizes research on image enhancement techniques in the spatial domain. It begins by introducing two categories of image enhancement - spatial domain and frequency domain methods. It then reviews various histogram equalization techniques for contrast enhancement in grayscale images, including global, local, and block-based approaches. The document also discusses extensions of histogram equalization as well as techniques that consider color images and use fuzzy logic approaches. It concludes that histogram equalization is commonly used for contrast enhancement but has limitations, and fuzzy logic methods show promise for color image enhancement.
User Interactive Color Transformation between ImagesIJMER
Abstract: In this paper we present a process called color
transfer which can borrow one image’s color
characteristics from another. Most current colorization
algorithms either require a significant user effort or have
large computational time. Here focus on orthogonal color
space i.e. lαβ color space without correlation between the
axes is given. Here we have implemented two global color
transfer algorithms in lαβ color space using simple color
statistical information such as mean, standard deviation
and covariance between the pixels of image. Our approach
is the extension of Reinhard's. Our local color transfer
algorithm uses simple color statistical analysis to recolor
the target image according to selected color range in
source image. Target image’s color influence mask is
prepared. It is a mask that specifies what parts of target
image will be affected according to selected color range.
After that target image is recolored in lαβ color space
according to prepared color influence map. In the lαβ
color space luminance and chrominance information is
separate so it allows making image recoloring optional.
The basic color transformation uses stored color statistics
of source and target image. All the algorithms are
implemented in JAVA object oriented language. The main
advantage of proposed method over the existing one is it
allows the user to recolor a part of the image in a simple &
intuitive way, preserving other color intact & achieving
natural look.
Index Terms: color transfer, local color statistics, color
characteristics, orthogonal color space, color influence
map.
Efficient Image Compression Technique using JPEG2000 with Adaptive ThresholdCSCJournals
Image compression is a technique to reduce the size of image which is helpful for transforms. Due to the limited communication bandwidth we have to need optimum compressed image with good visual quality. Although the JPEG2000 compression technique is ideal for image processing as it uses DWT (Discrete Wavelet Transform).But in this paper we proposed fast and efficient image compression scheme using JPEG2000 technique with adaptive subband threshold. Actually we used subband adaptive threshold in decomposition section which gives us more compression ratio and good visual quality other than existing compression techniques. The subband adaptive threshold that concentrates on denoising each subband (except lowest coefficient subbands) by minimizing insignificant coefficients and adapt with modified coefficients which are significant and more responsible for image reconstruction. Finally we use embedded block coding with optimized truncation (EBCOT) entropy coder that gives three different passes which gives more compressed image. This proposed method is compared to other existing approach and give superior result that satisfy the human visual quality and also these resulting compressed images are evaluated by the performance parameter PSNR.
Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis. For example, you can remove noise, sharpen, or brighten an image, making it easier to identify key features.
Here are some useful examples and methods of image enhancement:
Filtering with morphological operators, Histogram equalization, Noise removal using a Wiener filter, Linear contrast adjustment, Median filtering, Unsharp mask filtering, Contrast-limited adaptive histogram equalization (CLAHE). Decorrelation stretch
CONTRAST ENHANCEMENT AND BRIGHTNESS PRESERVATION USING MULTIDECOMPOSITION HIS...sipij
Histogram Equalization (HE) has been an essential addition to the Image Enhancement world.
Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization
(AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE)
methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final
image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization
(MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input
image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in
correct order. The final image after MDHE gives us the best results based on contrast enhancement and
brightness preservation aspect compared to all other techniques mentioned above. We have calculated the
various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported
by bar graphs, histograms and the parameter calculations at the end.
This document discusses various methods for contrast enhancement of images, including:
- Local color correction, which enhances contrast locally rather than globally.
- Simplest color balance, which clips a percentage of dark and light pixels before normalization.
- Screened Poisson equation, which acts as a high-pass filter using a single contrast parameter. Implementations of these methods in various color spaces like RGB, HSI, HSV, and HSL are provided. Local color correction is shown to perform better than global gamma correction by handling both dark and bright areas simultaneously.
This document discusses image segmentation techniques. It describes how segmentation partitions an image into meaningful regions based on discontinuities or similarities in pixel intensity. The key methods covered are thresholding, edge detection using gradient and Laplacian operators, and the Hough transform for global line detection. Adaptive thresholding is also introduced as a technique to handle uneven illumination.
Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retriev...IJERA Editor
This document evaluates the performance of the Euclidean and Manhattan distance metrics in a content-based image retrieval system. It finds that the Manhattan distance metric showed better precision than the Euclidean distance metric. The system uses color histograms and Gabor texture features to represent images. Color is represented in HSV color space and histograms of hue, saturation and value are used. Gabor filters are applied to capture texture at different scales and orientations. Distance between feature vectors is calculated using Euclidean and Manhattan distance formulas to find similar images from the database. The system was tested on a dataset of 1000 Corel images and Manhattan distance produced more relevant search results.
This document discusses digital image processing. It defines digital images as two-dimensional representations of values stored as pixels in computer memory. Digital image processing involves enhancing images, extracting information and features, and manipulating images using computer software. The document outlines common image processing techniques like image compression, enhancement, and measurement extraction. It also describes the basics of digital image editing using software to alter pixel values and change image properties.
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Image enhancement techniques can be divided into spatial and frequency domain methods. Spatial domain methods operate directly on pixel values using techniques like basic gray level transformations, contrast stretching and thresholding. These manipulations are used to accentuate image features, improve display quality or aid machine analysis by modifying pixel intensities within an image.
This document discusses various intensity transformation and spatial filtering techniques for digital image enhancement. It covers single pixel operations like negative image and contrast stretching. It also discusses neighborhood operations such as averaging and median filters. Finally, it discusses geometric spatial transformations like scaling, rotation and translation. The document provides details on basic intensity transformation functions including log, power law, and piecewise linear transformations. It also covers histogram processing techniques like histogram equalization, matching and local histogram processing. Spatial filtering and its mechanics are explained.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Histogram equalization (HE) is a simple and widely used image contrast enhancement technique. The basic disadvantage of HE is it changes the brightness of the image. In order to overcome this drawback, various HE methods have been proposed. These methods preserves the brightness on the output image but, does not have a natural look. In order to overcome this problem the, present paper uses Multi-HE methods, which decompose the image into several sub images, and classical HE method is applied to each sub image. The algorithm is applied on various images and has been analysed using both objective and subjective assessment.
This document compares image enhancement and analysis techniques using image processing and wavelet techniques on thermal images. It discusses various image enhancement methods such as converting images to grayscale, histogram equalization, contrast enhancement, linear and adaptive filtering, morphology, FFT transforms, and wavelet-based techniques including image fusion, denoising, and compression. Results showing enhanced, denoised, and compressed images are presented and analyzed. The document concludes that wavelet techniques provide better enhancement of thermal images compared to traditional image processing methods.
1) The document proposes a gradient-based method for low-light image enhancement. It extracts gradients from the input image, manipulates the gradients by applying higher gain to darker regions, and integrates the gradients while constraining the intensity range.
2) Experimental results show that the proposed method enhances low-light images effectively while avoiding saturation, compared to other techniques like histogram equalization.
3) The method runs in real-time and MATLAB code is available online for researchers.
This document discusses techniques for enhancing and analyzing thermal images using digital image processing. It begins with an overview of image enhancement, including highlighting details, removing noise, and increasing contrast. Thermal image enhancement is then discussed for applications in various fields. Key techniques covered include converting images to grayscale, histogram equalization, linear filtering for noise removal, morphology operations like erosion and dilation, and using fast Fourier transforms. A flowchart is proposed showing the sequence of applying these techniques to enhance an image.
This document provides an overview of various image enhancement techniques. It begins with an introduction to image enhancement and its objectives. It then outlines and describes several categories of enhancement methods, including spatial-frequency domain methods, point operations, histogram operations, spatial operations, and transform operations. Specific techniques discussed in detail include contrast stretching, clipping, thresholding, median filtering, unsharp masking, and principal component analysis for multispectral images. The document also covers color image enhancement and techniques for pseudocoloring.
The document discusses various image enhancement techniques in digital image processing. It describes point operations like image negative, contrast stretching, thresholding, brightness enhancement, log transformation, and power law transformation. Contrast stretching expands the range of intensity levels and can be done by multiplying pixels with a constant, using a transfer function, or histogram equalization. Thresholding converts an image to binary by assigning pixel values above a threshold to one level and below to another. Log and power law transformations compress high intensity values and expand low values to enhance an image. Matlab code examples are provided for each technique.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
At the end of this lesson, you should be able to;
identify color formation and how color visualize.
describe primary and secondary colors.
describe display on CRT and LCD.
comprehend RGB, CMY, CMYK and HSI color models.
Digital images can be enhanced in various ways to improve quality. There are three main categories of enhancement techniques: spatial domain, frequency domain, and combination methods. Spatial domain methods operate directly on pixel values using point processing or neighborhood filtering. Key spatial techniques include contrast stretching, thresholding, and histogram equalization. Frequency domain methods modify an image's Fourier transform. Common transformations include logarithmic, power-law, and piecewise linear functions, which can increase contrast or highlight certain grayscale ranges. Proper enhancement improves an image's features for desired applications.
A Comparative Study on Image Contrast Enhancement TechniquesIRJET Journal
This document presents a comparative study of various image contrast enhancement techniques. It discusses techniques like histogram equalization, gamma correction, brightness preserving bi-histogram equalization (BBHE), brightness preserving dynamic histogram equalization (BPDHE), and region based adaptive contrast enhancement (RACE). The study evaluates the performance of these techniques on different color images using objective parameters like entropy, absolute contrast error, and peak signal to noise ratio. The results show that the BPDHE technique generally produces enhanced images with less color error, higher contrast-to-noise ratio, and entropy values indicating more details compared to the other techniques. BPDHE is therefore found to be the best technique for enhancing image contrast while preserving color and brightness.
This document summarizes research on image enhancement techniques in the spatial domain. It begins by introducing two categories of image enhancement - spatial domain and frequency domain methods. It then reviews various histogram equalization techniques for contrast enhancement in grayscale images, including global, local, and block-based approaches. The document also discusses extensions of histogram equalization as well as techniques that consider color images and use fuzzy logic approaches. It concludes that histogram equalization is commonly used for contrast enhancement but has limitations, and fuzzy logic methods show promise for color image enhancement.
User Interactive Color Transformation between ImagesIJMER
Abstract: In this paper we present a process called color
transfer which can borrow one image’s color
characteristics from another. Most current colorization
algorithms either require a significant user effort or have
large computational time. Here focus on orthogonal color
space i.e. lαβ color space without correlation between the
axes is given. Here we have implemented two global color
transfer algorithms in lαβ color space using simple color
statistical information such as mean, standard deviation
and covariance between the pixels of image. Our approach
is the extension of Reinhard's. Our local color transfer
algorithm uses simple color statistical analysis to recolor
the target image according to selected color range in
source image. Target image’s color influence mask is
prepared. It is a mask that specifies what parts of target
image will be affected according to selected color range.
After that target image is recolored in lαβ color space
according to prepared color influence map. In the lαβ
color space luminance and chrominance information is
separate so it allows making image recoloring optional.
The basic color transformation uses stored color statistics
of source and target image. All the algorithms are
implemented in JAVA object oriented language. The main
advantage of proposed method over the existing one is it
allows the user to recolor a part of the image in a simple &
intuitive way, preserving other color intact & achieving
natural look.
Index Terms: color transfer, local color statistics, color
characteristics, orthogonal color space, color influence
map.
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International Journal of Engineering & Technical Research
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A New Algorithm for Human Face Detection Using Skin Color ToneIOSR Journals
This document presents a new algorithm for human face detection using skin color tone. The algorithm first resizes images and separates them into R, G, and B color bands, which are then transformed into the YCbCr color space and further into the YC'bC'r skin color tone space. Morphological processing is applied to make the images more accurate before determining the face area through projection. Experimental results show the algorithm can localize human faces in images with 92.69% accuracy by classifying pixels as skin or non-skin based on their YCbCr values.
Image Enhancement using Guided Filter for under Exposed ImagesDr. Amarjeet Singh
Image enhancement becomes an important step to
improve the quality of image and change in the appearance of
the image in such a way that either a human or a machine can
fetch certain information from the image after a change. Due
to low contrast images it becomes very difficult to get any
information out of it. In today’s digital world of imaging
image enhancement is a very useful in various applications
ranging from electronics printing to recognition. For highly
underexposed region, intensity bin are present in darken
region that’s by such images lacks in saturation and suffers
from low intensity. Power law transformation provides
solution to this problem. It enhances the brightness so as
image at least becomes visible. To modify the intensity level
histogram equalization can be used. In this we can apply
cumulative density function and probabilistic density function
so as to divide the image into sub images.
In proposed approach to provide betterment in
results guided filter has been applied to images after
equalization so that we can get better Entropy rate and
Coefficient of correlation can be improved with previously
available techniques. The guided filter is derived from local
linear model. The guided filter computes the filtering output
by considering the content of guidance image, which can be
the image itself or other targeted image.
Enhancement of Medical Images using Histogram Based Hybrid TechniqueINFOGAIN PUBLICATION
Digital Image Processing is very important area of research. A number of techniques are available for image enhancement of gray scale images as well as color images. They work very efficiently for enhancement of the gray scale as well as color images. Important techniques namely Histogram Equalization, BBHE, RSWHE, RSWHE (recursion=2, gamma=No), AGCWD (Recursion=0, gamma=0) have been used quite frequently for image enhancement. But there are some shortcomings of the present techniques. The major shortcoming is that while enhancement, the brightness of the image deteriorates quite a lot. So there was need for some technique for image enhancement so that while enhancement was done, the brightness of the images does not go down. To remove this shortcoming, a new hybrid technique namely RESWHE+AGCWD (recursion=2, gamma=0 or 1) was proposed. The results of the proposed technique were compared with the existing techniques. In the present methodology, the brightness did not decrease during image enhancement. So the results and the technique was validated and accepted. The parameters via PSNR, MSE, AMBE etc. are taken for performance evaluation and validation of the proposed technique against the existing techniques which results in better outperform.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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A comprehensive method for image contrast enhancement based on global –local ...eSAT Publishing House
This document presents a method for image contrast enhancement based on combining global and local contrast techniques. It addresses issues with existing local standard deviation based methods when applied to constant image areas, which results in divided by zero errors. The proposed method modifies the local standard deviation calculation by adding a small value to avoid this, allowing contrast enhancement to be applied across the entire image without information loss. Experimental results on test images demonstrate improved contrast enhancement over existing methods, as measured by peak signal-to-noise ratio values.
Recently image morphing is becoming a forefront subject and is attracting the attention of researchers. The motivation underpinning in exploring mage morphing is that it is producing wonderful effects on photographs and in film industries. Various morphing algorithms are been devised to cater for the challenges posed by new image requirements. So far in literature, warping algorithm has been applied individually to produce pleasing effects. However, the amalgamation of several algorithms using appropriate proportions has been put aside. In this paper, analysis of the mixture of morphing techniques has been applied on images to produce caricatures where the contours are cautiously preserved. The aesthetic effects of this newly devised amalgam algorithm is desirable to produce outstanding effects on face images.
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
ANALYSIS OF IMAGE ENHANCEMENT TECHNIQUES USING MATLABJim Jimenez
This document discusses various image enhancement techniques that can be implemented using MATLAB. It begins with an introduction to image processing and enhancement. Commonly used point operations like contrast stretching, gray level slicing, and histogram equalization are described. Histogram modelling is discussed in detail as an important enhancement technique. Adaptive histogram equalization is also covered. Finally, the implementation of some techniques using MATLAB is demonstrated, including generating and plotting histograms, regular and adaptive histogram equalization. Results are shown through images and histograms. The document concludes that histogram equalization is generally more powerful than other methods at improving image contrast and appearance.
The document discusses image processing and provides information on several key topics:
1. Image processing can be grouped into compression, preprocessing, and analysis. Preprocessing improves image quality by reducing noise and enhancing edges. Analysis extracts numeric or graphical information for tasks like classification.
2. Images are 2D matrices of intensity values represented by pixels. Common digital formats include grayscale, RGB, and RGBA. Higher bit depths allow more intensity levels to be represented.
3. Basic measurements of images include spatial resolution in pixels per unit, bit depth determining representable intensity levels, and factors like saturation and noise.
Comparative between global threshold and adaptative threshold concepts in ima...AssiaHAMZA
A digital image can be considered as a discrete representation of data possessing both spatial (layout) and
intensity (colour) information. Pixel intensities form a gateway communication between human perception
of things and digital image processing.
Image thresholding is a simple form of image segmentation. It is a way to create a binary image from a
grayscale or full-color image. This is typically done in order to separate "object" or foreground pixels from
background pixels to aid in image processing.
In this paper we aim to present a small and modest comparative between two kind of image thresholding.
The local and adapatative concepts may not give the same correct results at the end of a process, and we
aim to demonstrate which kind of the two
Colorization of Gray Scale Images in YCbCr Color Space Using Texture Extract...IOSR Journals
This document describes a technique for colorizing grayscale images by matching texture features between the grayscale image and windows in a color reference image. The technique works by first converting the images to the YCbCr color space, which has decorrelated color channels that allow color to be transferred without artifacts. Texture features like energy, entropy, homogeneity, contrast and correlation are then extracted from windows in the color image and compared to the grayscale image to find the best matching window. The mean and standard deviation of color values in the matching window are then imposed on pixels in the grayscale image to transfer color, while retaining the original luminance values. This process is repeated on small windows across the image to colorize the entire grayscale input.
The document is a project report on image contrast enhancement using histogram equalization and cubic spline interpolation. It discusses image processing and contrast enhancement techniques. It provides details on color models like RGB, HSV, and LAB. It describes converting between color spaces like RGB to HSV and RGB to LAB. It outlines histogram equalization and cubic spline interpolation for contrast enhancement in the spatial domain. The report was conducted as a training project at the Defence Terrain Research Laboratory in India.
This document discusses using genetic algorithms for image enhancement and segmentation. It begins with an overview of genetic algorithms and how they can be applied to optimization problems like image processing. Specifically, it describes how genetic algorithms use operators like crossover and mutation to evolve solutions over generations. It then discusses how genetic algorithms can be used for two main image processing tasks: image enhancement to improve image quality, and image segmentation to partition an image into meaningful regions. The key steps of the genetic algorithm for these tasks are described, including initializing a population, defining a fitness function, and applying genetic operators to evolve better solutions across generations.
Intensity Enhancement in Gray Level Images using HSV Color Coding TechniqueIRJET Journal
This document discusses techniques for enhancing the intensity of gray scale images using HSV color space coding. It begins with an abstract discussing the motivation to increase image clarity and reduce errors from fatigue. Section 1 provides an introduction to image processing and enhancement. Section 1.1 discusses digital images, including types such as black and white, color, binary, and indexed color images. Section 2 covers hardware used in image processing like lights. Section 3 discusses linear filters that can perform operations like smoothing and sharpening through convolution.
Similar to Establishment of an Efficient Color Model from Existing Models for Better Gamma Encoding In Image Processing (20)
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
How to Create a More Engaging and Human Online Learning Experience
Establishment of an Efficient Color Model from Existing Models for Better Gamma Encoding In Image Processing
1. T. M. Shahriar Sazzad, Sabrin Islam, Mohammad Mahbubur Rahman Khan Mamun & Md. Zahid Hasan
International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 90
Establishment of an Efficient Color Model from Existing Models
for Better Gamma Encoding In Image Processing
T. M. Shahriar Sazzad tss5standrews@gmail.com
Department of Computer Science
University of St Andrews
St Andrews, UK
Sabrin Islam islamsabrin@yahoo.com
Department of Computer Science
American International University Bangladesh
Dhaka,Bangladesh
Mohammad Mahbubur Rahman Khan Mamun siha24@gmail.com
EEE, BUET
Dhaka, Bangladesh
Md. Zahid Hasan hasan.ice@gmail.com
Lecturer, Dept. of CSE
Green University
Dhaka,Bangladesh
Abstract
Human vision is an important factor in the areas of image processing. Research has been done
for years to make automatic image processing but still human intervention can not be denied and
thus better human intervention is necessary. Two most important points are required to improve
human vision which are light and color. Gamma encoder is the one which helps to improve the
properties of human vision and thus to maintain visual quality gamma encoding is necessary.
It is to mention that all through the computer graphics RGB (Red, Green, and Blue) color space is
vastly used. Moreover, for computer graphics RGB color space is called the most established
choice to acquire desired color. RGB color space has a great effort on simplifying the design and
architecture of a system. However, RGB struggles to deal efficiently for the images those belong
to the real-world.
Images are captured using cameras, videos and other devices using different magnifications. In
most cases during processing, in compare to the original outlook the images appear either dark
or bright in contrast. Human vision affects and thus poor quality image analysis may occur.
Consequently this poor manual image analysis may have huge difference from the computational
image analysis outcome. Question may arise here why we will use gamma encoding when
histogram equalization or histogram normalization can enhance images. Enhancing images does
not improve human visualization quality all the time because sometimes it brightens the image
quality when it is needed to darken and vice-versa. Human vision reflects under universal
illumination environment (not pitch black or blindingly bright) thus follows an approximate gamma
or power function. Hence, this is not a good idea to brighten images all the time when better
human visualization can be obtained while darkening the images. Better human visualization is
important for manual image processing which leads to compare the outcome with the semi-
automated or automated one. Considering the importance of gamma encoding in image
processing we propose an efficient color model which will help to improve visual quality for
manual processing as well as will lead analyzers to analyze images automatically for comparison
and testing purpose.
2. T. M. Shahriar Sazzad, Sabrin Islam, Mohammad Mahbubur Rahman Khan Mamun & Md. Zahid Hasan
International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 91
Keywords: Gamma, Human Vision, RGB, HSI, HSB, Light.
1. INTRODUCTION
A color space can be defined as the mathematical illustration of a set of colors. In the areas of
image processing there are different color models available of which RGB (mainly used for
computer graphics), YUV, YIQ, or YCbCr (used for video systems) and CMYK (used for color
printing) are most popular. However, it is to mention that, for instinctive ideas of hue, saturation
and brightness; the above three color models are not directly related at all. For this perspective,
HSI, HSV or HSB are suitable color models for programming simplicity, end user manipulation
and processing purposes although all of these color models is derived from the RGB information
supplied by devices such as cameras and scanners [1,2,3,4].
Color Model Classifications
Munsell Device dependent
RGB, CMY(K) Device dependent
YIQ,YUV, YCbCr Device dependent
HSI, HSV, HSL User oriented-Device
dependent
CIE XYZ, CIE L*U*V*,
CIE L*a*b*
Device independent, color
Metric
Table 1: Color Models Classifications.
Color Model Application Area
Munsell Human visual system
RGB Computer graphics, Image
processing, Analysis,
Storage
CMY(K) Printing
YIQ, YUV TV broadcasting, Video
system
YCbCr Digital video
HSI, HSV, HSL Human visual perception,
Computer graphics,
processing, Computer
Vision, Image Analysis,
Design image, Human
vision, Image editing
software, Video editor
CIE XYZ ,CIE L*U*V*,
CIE L*a*b*
Evaluation of color
difference, Color matching
system, advertising, graphic
arts, digitized or animated
paintings, multimedia
products
Table 2: Application Areas of Color Models.
It is to mention that all through the computer graphics RGB (Red, Green, and Blue) color space is
vastly used. Moreover, for computer graphics RGB color space is called the most established
choice to acquire desired color. RGB color space has a great effort on simplifying the design and
architecture of a system. However, RGB struggles to deal efficiently for the images those belong
3. T. M. Shahriar Sazzad, Sabrin Islam, Mohammad Mahbubur Rahman Khan Mamun & Md. Zahid Hasan
International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 92
to the real-world. Moreover, processing images with the help of RGB color model is not an
efficient method either.
Various types of color model have been established already. One main color model is RGB color
model where 3 different colors are added together in different ways to produce a wide range of
colors. As for example for a 24 bit RGB color image, a total number of colors can be (2
8
)
3
=
16,777,216.
RGB color model is used to represent and display images in electronic systems. It is to mention
that RGB color model is device dependent as Red, Green and Blue levels are different from
manufacturers to manufacturers. Sometimes these colors vary even in same devices over a period
of time and hence without a color management RGB color value does not acts as same in devices.
To display RGB colors in hardware a display card named cathode ray tube (CRT) is used to
handle the numeric RGB color values and in most CRT displays do have a power-law transfer
characteristic with a gamma of about 2.5. In most occasions it has been observed that gamma
remains out of consideration. Under these circumstances, an accurate reproduction of the original
scene results in an image that human viewers judge as "flat" and lacking in contrast.
To improve the quality of visual perception for color images, the term image enhancement is an
important factor. Image enhancement is needed in many areas such as photography, scanning,
image analysis etc. Image enhancement approaches fall into two broad categories such as spatial
domain and frequency domain methods. The term spatial domain refers to the image plane itself,
and approaches in this category are based on direct manipulation of pixels in an image whereas
frequency domain processing techniques are based on modifying the Fourier transform of an
image.
Color image enhancement is considered the most frequently used method these days using
adaptive neighborhood histogram equalization technique [14]. 3D histogram equalization has been
proposed using RGB cube [15]. A new approach considering enhancement problem has been
established [13, 20]
There are some more techniques available for wavelength based image enhancement which helps
to enhances the image edges [19]. It is generally unwise to histogram equalize the components of
a color image independently because it causes erroneous color. A more logical approach is
histogram normalization while spreading the color intensities uniformly, leaving the color
themselves (eg. Hue) enhanced.
Images can be gray-level images or color images. Comparing with color images gray-level images
have got only one value for each pixel as images are made with pixel representation. There are
many existing algorithm available which helps to enhance the image contrast for gray-level images
considering piecewise-linear transformation function named contrast stretching with normalization,
stretching with histogram techniques. Most of these available algorithm are not suitable for color
images although they are used widely having poor quality and distorted effects [5].
Gray level transformation is proved to be better approach than any other transformation and hence
most proposed methods are based on spatial domain approach. Image enhancement using spatial
domain works with gray-level transformation or power law transformation. Power law equation is
referred to as gamma.
crS
γ
= ; where c and r are positive constants. Value of c= 1 and the value of gamma can
vary to set the desired result and the process used to correct power-law transformation
phenomena is called gamma correction or gamma encoding.
However, it is to mention that, only enhancing the image does not improve the image quality for
better visual perception. Sometimes it is needed to darken the bright images to obtain a better
visualization [6]. Gamma is one of the main factor which helps to brighten or darken an image.
The above mentioned techniques are widely used in the areas of image enhancement without
much considering the color shifting issues. A color image enhancement technique should not
change a pixel value from red to yellow as an example although in some cases color shifting may
be necessary while controlling them before it can be applied. Hue is one of the main properties of
a color and hence it is not easy to control hue in color enhancement especially in RGB color
4. T. M. Shahriar Sazzad, Sabrin Islam, Mohammad Mahbubur Rahman Khan Mamun & Md. Zahid Hasan
International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 93
model. The color shifting issue has been considered in some research by Gupta et al, Naik et al
where it has been suggested that hue should be preserved while applying image enhancement
method [16, 17, 18]. These methods keeps hue preserved and avoids color shifting but still there
are problems. However, enhancement does not resolve human visualization perfectly because
sometimes images need to make dark instead of enhancement. In that case enhancement does
not help at all.
To resolve the above mentioned for human visualization considering two issues 1) color shifting
and 2) human visualization we have come up with an idea that gamma encoding is necessary
while decomposing the luminance (is an objective term and it is a measure of the amount of light
coming off from a source, or reflected from an object) or brightness ( perception of how much light
is coming from a source or an object, and depends upon the context as well as the luminance) and
for saturation instead of histogram equalization, histogram normalization can be applied.
This research aspires to establish an efficient color model for better gamma encoding in image
processing from all the existing color models available at this moment.
2. METHODOLOGY
Our proposed gamma encoding technique is based on spatial domain instead of frequency
domain approach.
In RGB color model, there are three primary colors considered named Red, Green and Blue
where RGB is defined as additive or subtractive model and hence different colors can be
preformed using the combination of these primary colors. But for HIS (hue, saturation, intensity)
and HSV (hue, saturation, value) or HSB (hue, saturation, brightness) color spaces were
developed to distinguish and understand color by human. Hue is the main attribute of a color and
thus decides which color the pixel has obtained. However, hue should not be changed at any
point because changing the hue changes the color as well as distortion occurs in the image.
Moreover, comparing with color space like CIE LUV and CIE Lab, in HSB it is easy to control hue
and color shifting. Our main approach is to preserve the hue and apply better human visualization
using saturation and brightness and hence we have chosen HSB color space instead of other
color space [21, 22, 23].
It is to mention that for traditional image processing such as histograms, equalization HSI color
space is one of the best model [7]. However, HSB color space is one of the best for manipulating
hue and saturation (to shift colors or adjust the amount of color) and thus it capitulates a better
active range of saturation [8].
3. COLOR MODEL CONVERSION
2.1 RGB to HSB
Below equations describes the conversion from RGB to HSB color space. For easier definition we
have used maximum and minimum component values as M and m respectively and R for Red, G
for Green and B for Blue and C is the difference between maximum and minimum.
M = max ( R , G , B ) ( 1 )
M = min ( R , G , B ) ( 2 )
C = M – n ( 3 )
Hue is the proportion of the distance around the edge of the hexagon which passes through the
projected point, measured on the range [0,1] or in degree [0,360]. Mathematical expression for
hue is
5. T. M. Shahriar Sazzad, Sabrin Islam, Mohammad Mahbubur Rahman Khan Mamun & Md. Zahid Hasan
International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 94
=+
−
=+
−
=
−
=
=
BMif
C
GR
GMif
C
RB
RMif
C
BG
CifUndefined
H
,4
,2
,6mod
0,
' ( 4 )
'
60 HxH o
= ( 5 )
2.2 HSB to RGB
Below equations describes the conversions from HSB to RGB.
o
H
H
60
'
= ( 6 )
( )12mod1 '
−−= HCX ( 7 )
<≤
<≤
<≤
<≤
<≤
<≤
=
35),0,(
54),0,(
43),,0(
32),,0(
21)0,,(
20)0,,(
)0,0,0(
),,(
'
'
'
'
'
'
111
HifXC
HifCX
HifCX
HifXC
HifCX
HifXC
UndefinedisHif
BGR
( 8 )
m= Y’ – (0.30R1 + 0.59 G1 + 0.11 B1) ( 9 )
(R,G,B)=(R1+m,G1+m,B1+m) ( 10 )
This is a geometric warping of hexagons into circles where each side of the hexagon is mapped
onto a 60 degree arc of thecircle.
S=0, if C=0 ( 11 )
S=1- min / max, otherwise ( 12 )
S is denoted for saturation
)(
3
1
BGRI ++= ( 13 )
where I is denoted as intensity
B = max ( 14 )
where B is denoted in HSB as brightness.
2.3 RGB to HSI
Equation (1) describes the conversion from RGB to HSI color space.
6. T. M. Shahriar Sazzad, Sabrin Islam, Mohammad Mahbubur Rahman Khan Mamun & Md. Zahid Hasan
International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 95
)(
3
1
BGRI ++= (15)
[ ]),,min(
)(
3
1 BGR
BGR
S
++
−= (16)
[ ]
−−+−
−+
= −
))(()(
))()_((5.0
cos
2
1
BGBRGR
BRGR
H (17)
If B is greater than G, then H=360
o
-H (18)
Where R, G and B are three color component of source RGB image, H, S and I it’s components
of hardware independent on HSI format
2.4 HSI to RGB
As it can be seen that conversion from RGB to HSI is not easy with regard to computing algorithm
complexity because it's regarding minimum from three searching (expression 1, as minimum two
operators of condition), long cosine function, square root, square computation, additional
operation of condition (expression 4) during one pixel conversion. Moreover, it is difficult to
convert from HSI color space to standard RGB, where the process depends on which color sector
H lies in. For the RG sector (0
0
≤ H ≤120
0
), we have the following equations to convert RGB to
HSI format:
B=I(1-S) (19)
−
+=
)60cos(
cos
1 0
H
HS
IR (20)
G = 3I − (R + B) (21)
For the GB sector (120
0
≤H ≤240
0
):
H = H −120
0
(22)
R = I (1− S) (23)
−
+=
)60cos(
cos
1 0
H
HS
IG (24)
B = 3I − (R +G) (25)
7. T. M. Shahriar Sazzad, Sabrin Islam, Mohammad Mahbubur Rahman Khan Mamun & Md. Zahid Hasan
International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 96
For the BR sector (240
0
≤H ≤360
0
):
H = H − 240
0
(26)
G = I (1− S) (27)
−
+=
)60cos(
cos
1
H
HS
IB o
(28)
R = 3I − (G + B) (29)
4. GAMMA ENCODER
It is wise to use luma which represents the brightness in an image and can be denoted as Y.
Luma is weighted average of gamma-encoding which can be denoted as Y’ for R,G and B and
hence denoted as R’G’B’.
The equation becomes,
Y=0.2126R+0.7152G+0.0722B for luminance
Y’=0.2126R’+0.7152G’+0.0722B’ for gamma encoding
5. SATURATION
To make the color image soft and better human acceptance it is necessary to use saturation
adjustment. We have applied histogram normalization instead of histogram equalization because
normalize models stretches image pixel values to cover the entire pixel value range from (0-255)
whereas equalize module attempts to equalize the number of pixels in a given color thus uses a
single row of pixels.
6. PROCESSING STEPS
FIGURE 1: Block Diagram of Proposed Work.
8. T. M. Shahriar Sazzad, Sabrin Islam, Mohammad Mahbubur Rahman Khan Mamun & Md. Zahid Hasan
International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 97
7. EXPERIMENTAL RESULTS
To test the performance of our proposed approach we have used three different contrast color
images (low contrast or darker from the original outlook, medium contrast or similar to original
outlook and high contrast or brighter than original outlook color images). To evaluate the contrast
performance we have applied histogram normalization saturation value from 0.4 – 0.6 and
gamma correction value ranges from 0.75 – 2.2 in different computers as different computers acts
different according to gamma value. It is to mention that gamma value > 1 performs darkening
and vice-versa [9, 10, 11, 12].
Figure 2, 3 and 4 images with (a),(b),(c) illustrates that (a) is the original image, (b) is the
experimental result obtained using HSI and (c) is the experimental result obtained using HSB.
FIGURE 2
FIGURE 3
FIGURE 4
Images used Using HSI
(acceptance rate
from users)
Using HSB
(acceptance rate
from users)
Comparison
result
Bright Images (Total 223 images ) 83 % 88 % HSB acceptance
rate is high
Dark Images (Total 304 Images ) 79 % 89 % HSB acceptance
rate is high
TABLE 3: Detailed comparison between existing approach without gamma and our proposed approach with
accuracy. Sample results were collected considering human visual perception.
9. T. M. Shahriar Sazzad, Sabrin Islam, Mohammad Mahbubur Rahman Khan Mamun & Md. Zahid Hasan
International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 98
FIGURE 5: (Represents Table 3 in Graphical Form).
From the above Table 3 and Fig: 5; it is clear that HSB works better in compare to HSI for both
bright and dark images. Moreover, for dark images using HSI only 79% accuracy is obtained
whereas using HSB 89% accuracy has been obtained which proves that especially for dark
images use of HSB will be the best approach for image enhancement. For bright images there is
accuracy difference of 5% between HSI and HSB and hence it can be said that HSB performs
better. However, special care is important when enhancing bright images.
8. CONCLUSION
This paper has proposed an efficient color model for better gamma encoding in image processing
from all the existing color models available at this moment. It is difficult to judge an enhanced
image result even with a subjective assessment. We claim that HSB color model is more robust
than HSI color model or from others because others do produces unrealistic colors and/or over
enhanced resultant images. However, there may be still some areas needs to be taken care of as
the color enhancement needs to change or shift color using hue although these cases are
exceptional and very rare.
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International Journal of Image Processing (IJIP), Volume (7): Issue (1): 2013 99
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