Image enhancement technique plays vital role in improving the quality of the image. Enhancement
technique basically enhances the foreground information and retains the background and improve the
overall contrast of an image. In some case the background of an image hides the structural information of
an image. This paper proposes an algorithm which enhances the foreground image and the background
part separately and stretch the contrast of an image at inter-object level and intra-object level and then
combines it to an enhanced image. The results are compared with various classical methods using image
quality measures
Contrast enhancement using various statistical operations and neighborhood pr...sipij
Ā
Histogram Equalization is a simple and effective contrast enhancement technique. In spite of its popularity
Histogram Equalization still have some limitations āproduces artifacts, unnatural images and the local
details are not considered, therefore due to these limitations many other Equalization techniques have been
derived from it with some up gradation. In this proposed method statistics play an important role in image
processing, where statistical operations is applied to the image to get the desired result such as
manipulation of brightness and contrast. Thus, a novel algorithm using statistical operations and
neighborhood processing has been proposed in this paper where the algorithm has proven to be effective in
contrast enhancement based on the theory and experiment.
OBJECT SEGMENTATION USING MULTISCALE MORPHOLOGICAL OPERATIONSijcseit
Ā
Object segmentation plays an important role in human visual perception, medical image processing and content based image retrieval. It provides information for recognition and interpretation. This paper uses mathematical morphology for image segmentation. Object segmentation is difficult because one usually does not know a priori what type of object exists in an image, how many different shapes are there and what regions the image has. To carryout discrimination and segmentation several innovative segmentation methods, based on morphology are proposed. The present study proposes segmentation method based on multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.The method is tested on various datasets and results shows that it can be used for both interactive and automatic segmentation.
Review on Image Enhancement in Spatial Domainidescitation
Ā
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.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
Contrast enhancement using various statistical operations and neighborhood pr...sipij
Ā
Histogram Equalization is a simple and effective contrast enhancement technique. In spite of its popularity
Histogram Equalization still have some limitations āproduces artifacts, unnatural images and the local
details are not considered, therefore due to these limitations many other Equalization techniques have been
derived from it with some up gradation. In this proposed method statistics play an important role in image
processing, where statistical operations is applied to the image to get the desired result such as
manipulation of brightness and contrast. Thus, a novel algorithm using statistical operations and
neighborhood processing has been proposed in this paper where the algorithm has proven to be effective in
contrast enhancement based on the theory and experiment.
OBJECT SEGMENTATION USING MULTISCALE MORPHOLOGICAL OPERATIONSijcseit
Ā
Object segmentation plays an important role in human visual perception, medical image processing and content based image retrieval. It provides information for recognition and interpretation. This paper uses mathematical morphology for image segmentation. Object segmentation is difficult because one usually does not know a priori what type of object exists in an image, how many different shapes are there and what regions the image has. To carryout discrimination and segmentation several innovative segmentation methods, based on morphology are proposed. The present study proposes segmentation method based on multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.The method is tested on various datasets and results shows that it can be used for both interactive and automatic segmentation.
Review on Image Enhancement in Spatial Domainidescitation
Ā
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.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
A New Method for Indoor-outdoor Image Classification Using Color Correlated T...CSCJournals
Ā
In this paper a new method for indoor-outdoor image classification is presented; where the concept of Color Correlated Temperature is used to extract distinguishing features between the two classes. In this process, using Hue color component, each image is segmented into different color channels and color correlated temperature is calculated for each channel. These values are then incorporated to build the image feature vector. Besides color temperature values, the feature vector also holds information about the color formation of the image. In the classification phase, KNN classifier is used to classify images as indoor or outdoor. Two different datasets are used for test purposes; a collection of images gathered from the internet and a second dataset built by frame extraction from different video sequences from one video capturing device. High classification rate, compared to other state of the art methods shows the ability of the proposed method for indoor-outdoor image classification.
Developing 3D Viewing Model from 2D Stereo Pair with its Occlusion RatioCSCJournals
Ā
We intend to make a 3D model using a stereo pair of images by using a novel method of local matching in pixel domain for calculating horizontal disparities. We also find the occlusion ratio using the stereo pair followed by the use of The Edge Detection and Image SegmentatiON (EDISON) system, on one the images, which provides a complete toolbox for discontinuity preserving filtering, segmentation and edge detection. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. We then warp the segment disparities to the original image to get our final 3D viewing Model.
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
Ā
Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.
Content Based Image Retrieval using Color Boosted Salient Points and Shape fe...CSCJournals
Ā
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. Most of these detectors are luminance based which have the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. This paper presents a method for salient points determination based on color saliency. The color and texture information around these points of interest serve as the local descriptors of the image. In addition, the shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the local color, texture and the global shape features provides a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Ā
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.
A Survey on Exemplar-Based Image Inpainting Techniquesijsrd.com
Ā
Preceding paper include exemplar-based image inpainting technique give idea how to inpaint destroyed region such as Criminisi algorithm, patch shifting scheme, search region prior method. CriminsiĆĀ¢Ć¢āĀ¬Ć¢āĀ¢s and SarawutĆĀ¢Ć¢āĀ¬Ć¢āĀ¢s patch shifting scheme needed more time to inpaint an damaged region but proposed method decrease time complexity by searching only in related region of missing portion of image.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Study of Image Inpainting Technique Based on TV Modelijsrd.com
Ā
This paper is related with an image inpainting method by which we can reconstruct a damaged or missing portion of an image. A fast image inpainting algorithm based on TV (Total variational) model is proposed on the basis of analysis of local characteristics, which shows the more information around damaged pixels appears, the faster the information diffuses. The algorithm first stratifies and filters the pixels around damaged region according to priority, and then iteratively inpaint the damaged pixels from outside to inside on the grounds of priority again. By using this algorithm inpainting speed of the algorithm is faster and greater impact.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
Ā
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
The development of multimedia system technology in Content based Image Retrieval (CBIR) System is
one in every of the outstanding area to retrieve the images from an oversized collection of database. The feature
vectors of the query image are compared with feature vectors of the database images to get matching images.It is
much observed that anyone algorithm isn't beneficial in extracting all differing kinds of natural images. Thus an
intensive analysis of certain color, texture and shape extraction techniques are allotted to spot an efficient CBIR
technique that suits for a selected sort of images. The Extraction of an image includes feature description and
feature extraction. During this paper, we tend to projected Color Layout Descriptor (CLD), grey Level Co-
Occurrences Matrix (GLCM), Marker-Controlled Watershed Segmentation feature extraction technique that
extract the matching image based on the similarity of Color, Texture and shape within the database. For
performance analysis, the image retrieval timing results of the projected technique is calculated and compared
with every of the individual feature.
Object-Oriented Approach of Information Extraction from High Resolution Satel...iosrjce
Ā
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Ā
Object segmentation plays an important role in human visual perception, medical image processing and
content based image retrieval. It provides information for recognition and interpretation. This paper uses
mathematical morphology for image segmentation. Object segmentation is difficult because one usually
does not know a priori what type of object exists in an image, how many different shapes are there and
what regions the image has. To carryout discrimination and segmentation several innovative segmentation
methods, based on morphology are proposed. The present study proposes segmentation method based on
multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment
simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.
The method is tested on various datasets and results shows that it can be used for both interactive and
automatic segmentation.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Ā
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
A New Method for Indoor-outdoor Image Classification Using Color Correlated T...CSCJournals
Ā
In this paper a new method for indoor-outdoor image classification is presented; where the concept of Color Correlated Temperature is used to extract distinguishing features between the two classes. In this process, using Hue color component, each image is segmented into different color channels and color correlated temperature is calculated for each channel. These values are then incorporated to build the image feature vector. Besides color temperature values, the feature vector also holds information about the color formation of the image. In the classification phase, KNN classifier is used to classify images as indoor or outdoor. Two different datasets are used for test purposes; a collection of images gathered from the internet and a second dataset built by frame extraction from different video sequences from one video capturing device. High classification rate, compared to other state of the art methods shows the ability of the proposed method for indoor-outdoor image classification.
Developing 3D Viewing Model from 2D Stereo Pair with its Occlusion RatioCSCJournals
Ā
We intend to make a 3D model using a stereo pair of images by using a novel method of local matching in pixel domain for calculating horizontal disparities. We also find the occlusion ratio using the stereo pair followed by the use of The Edge Detection and Image SegmentatiON (EDISON) system, on one the images, which provides a complete toolbox for discontinuity preserving filtering, segmentation and edge detection. Instead of assigning a disparity value to each pixel, a disparity plane is assigned to each segment. We then warp the segment disparities to the original image to get our final 3D viewing Model.
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
Ā
Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.
Content Based Image Retrieval using Color Boosted Salient Points and Shape fe...CSCJournals
Ā
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. Most of these detectors are luminance based which have the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. This paper presents a method for salient points determination based on color saliency. The color and texture information around these points of interest serve as the local descriptors of the image. In addition, the shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the local color, texture and the global shape features provides a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Ā
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.
A Survey on Exemplar-Based Image Inpainting Techniquesijsrd.com
Ā
Preceding paper include exemplar-based image inpainting technique give idea how to inpaint destroyed region such as Criminisi algorithm, patch shifting scheme, search region prior method. CriminsiĆĀ¢Ć¢āĀ¬Ć¢āĀ¢s and SarawutĆĀ¢Ć¢āĀ¬Ć¢āĀ¢s patch shifting scheme needed more time to inpaint an damaged region but proposed method decrease time complexity by searching only in related region of missing portion of image.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Study of Image Inpainting Technique Based on TV Modelijsrd.com
Ā
This paper is related with an image inpainting method by which we can reconstruct a damaged or missing portion of an image. A fast image inpainting algorithm based on TV (Total variational) model is proposed on the basis of analysis of local characteristics, which shows the more information around damaged pixels appears, the faster the information diffuses. The algorithm first stratifies and filters the pixels around damaged region according to priority, and then iteratively inpaint the damaged pixels from outside to inside on the grounds of priority again. By using this algorithm inpainting speed of the algorithm is faster and greater impact.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
Ā
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
The development of multimedia system technology in Content based Image Retrieval (CBIR) System is
one in every of the outstanding area to retrieve the images from an oversized collection of database. The feature
vectors of the query image are compared with feature vectors of the database images to get matching images.It is
much observed that anyone algorithm isn't beneficial in extracting all differing kinds of natural images. Thus an
intensive analysis of certain color, texture and shape extraction techniques are allotted to spot an efficient CBIR
technique that suits for a selected sort of images. The Extraction of an image includes feature description and
feature extraction. During this paper, we tend to projected Color Layout Descriptor (CLD), grey Level Co-
Occurrences Matrix (GLCM), Marker-Controlled Watershed Segmentation feature extraction technique that
extract the matching image based on the similarity of Color, Texture and shape within the database. For
performance analysis, the image retrieval timing results of the projected technique is calculated and compared
with every of the individual feature.
Object-Oriented Approach of Information Extraction from High Resolution Satel...iosrjce
Ā
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Ā
Object segmentation plays an important role in human visual perception, medical image processing and
content based image retrieval. It provides information for recognition and interpretation. This paper uses
mathematical morphology for image segmentation. Object segmentation is difficult because one usually
does not know a priori what type of object exists in an image, how many different shapes are there and
what regions the image has. To carryout discrimination and segmentation several innovative segmentation
methods, based on morphology are proposed. The present study proposes segmentation method based on
multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment
simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.
The method is tested on various datasets and results shows that it can be used for both interactive and
automatic segmentation.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Ā
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
Performance of Efficient Closed-Form Solution to Comprehensive Frontier Exposureiosrjce
Ā
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image Segmentation Based Survey on the Lung Cancer MRI ImagesIIRindia
Ā
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image Enhancement using Guided Filter for under Exposed ImagesDr. Amarjeet Singh
Ā
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 todayās 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 thatās 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.
A binarization technique for extraction of devanagari text from camera based ...sipij
Ā
This paper presents a binarization method for camera based natural scene (NS) images based on edge
analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried
out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard
deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes.
The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are
compared with other standard techniques. The method is fast and works well for camera based natural
scene images.
A novel approach to Image Fusion using combination of Wavelet Transform and C...IJSRD
Ā
Panchromatic furthermore multi-spectral image fusion outstands common methods of high-resolution color image amalgamation. In digital image reconstruction, image fusion is standout pre-processing step that aims increasing hotspot image quality to extricate all suitable information from source images ruining inconsistencies or artifacts. Around the different strategies available for image fusion, Wavelet and Curvelet based algorithms are mostly preferred. Wavelet transform is useful for point singularities while Curvelet transform, as the name describes, is more useful for the analysis of images having curved shape edges. This paper reveals a study of development in the field of image fusion.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
Ā
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Ā
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Ā
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Ā
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But thereās more:
In a second workflow supporting the same use case, youāll see:
Your campaign sent to target colleagues for approval
If the āApproveā button is clicked, a Jira/Zendesk ticket is created for the marketing design team
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Object based image enhancement
1. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
DOI : 10.5121/ijait.2014.4302 9
OBJECT-BASED IMAGE ENHANCEMENT
TECHNIQUE FOR GRAY SCALE IMAGES
G. Srinivasa Rao1
Dr. A. Sri Krishna2
Dr. S. Mahaboob Basha3
Ch. Jeevan
Prakash4
Department of Information Technology, R.V.R & J.C College of Engineering,
Guntur, Andhra Pradesh
ABSTRACT
Image enhancement technique plays vital role in improving the quality of the image. Enhancement
technique basically enhances the foreground information and retains the background and improve the
overall contrast of an image. In some case the background of an image hides the structural information of
an image. This paper proposes an algorithm which enhances the foreground image and the background
part separately and stretch the contrast of an image at inter-object level and intra-object level and then
combines it to an enhanced image. The results are compared with various classical methods using image
quality measures.
KEYWORDS
Image Enhancement, Morphological watershed segmentation, Object-based contrast enhancement, Inter-
object stretching, Intra-object stretching.
1. INTRODUCTION
Basically image enhancement [3] improves the quality of the image so that the result is more
suitable for a specific application and for human perception. Image enhancement techniques are
widely used in many real time applications. The contrast enhancement in digital images can be
handled by using various point processing techniques[2]-[7] like power law, logarithmic
transformations and histogram equalization(HE).Image enhancement using power law
transformations depends upon the gamma values, if the gamma value exceeds 1, the contrast is
reduced.The logarithmic transformation [2]-[4] improve the contrast of the image, but increases
the overall brightness.The most widely used technique of Contrast enhancement is Histogram
Equalization (HE)[1]-[5], which works by flattening the histogram and stretching the dynamic
range of the gray-levels using the cumulative density function of the image. However, there are
some drawbacks with histogram equalization [8] especially when implemented to process digital
images. Firstly, it converts the histogram of the original image into a uniform histogram with a
mean value at the middle of gray level range. So, the average intensity value of the output image
is always at the middle ā or close to it. In the case of images with high and low average intensity
values, there is a significant change in the image outlook after enhancing the contrast and some
noise is also introduced into the image. Secondly, histogram equalization enhances the image
based on the global content of the image and in its discrete form large bins cannot be broken and
reordered to produce the desired uniform histogram. In other words, HE is powerful in
highlighting the borders and edges between different objects, but it may reduce the local details
within these objects, particularly smooth and small ones. One more consequence for this
2. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
10
mergence between large and small bins is the creation of over enhancement and saturation
artifacts [10]. Recently a histogram equalization based contrast enhancement has been proposed
[24], which improve the image in poor lightning. Many attempts have been made so far to
improve the performance of Histogram Equalization [3],[8]-[10],[24].
Even though image enhancement techniques, generally not required for automated analysis
techniques, have regained a significant interest in recent years. Most of the existing automatic
enhancement techniques make use of global intensity transforms, either for color correction
(white balancing) or contrast enhancement. For these global intensity transforms, the mapping of
color or intensity is one-to-one and is independent of pixel location or scene context. Such
techniques would not work well for images where different parts of the image require different
types of correction, e.g., the darker portions of an indoor scene requires higher contrast tuning and
different Color adjustment than the window, or it is necessary to highlight the subject by
enhancing contrast between the subject and its background [1]. The Object Based Multi-Level
Contrast Stretching Method dividing the input image into its constituent objects, and apply
stretching strategies based on type of the objects. This object-based multilevel enhancement
method can produce enhanced images without ringing, blocking and false contouring artifacts.
This paper proposes an object based technique to enhance the local contrast in the spatial
domain. It uses morphological opening operation for finding gradient thresholding. The paper is
organized as follows. Section 2 describes the Basic Terminology. Section 3 describes Object
Based Contrast Stretching (OBCS). Section 4 presents the Comparison Of Object-Based Contrast
Stretching with existing methods. Section 5 presents conclusions.
2. BASIC TERMINOLOGY
2.1. MORPHOLOGICAL OPERATIONS
Mathematical morphology [6] is the part of set theory, which has a robust geometric orientation.
Mathematical morphology presents a well-found theory for analysis and processing for binary
images.
Consider a 2D digital image Aā ZĆZ and a point u ā ZĆZ, the transition of A by u is given by
equation (1)
(1)
Dilation and Erosion are the fundamental morphological operations which are given by (2) and
(3) respectively
(2)
(3)
Here B is a structuring element.
In terms of dilation and erosion another pair of morphological operations are defined known as
closing and opening given by (4) and (5) respectively..
3. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
11
(4)
(5)
.
2.2. EDGE DETECTION
An edge [7] is a connected set of pixels on the boundary between two regions. There are many
ways to perform edge detection. The edge detection is categorized into two categories.
1) The First Order Derivative.
2) Second Order Derivative.
The First Order Derivative is a gradient method which detects the edges by looking for the
magnitude of gradient in the image, and the Laplacian method searches for zero crossings in the
second derivative of the image is used to find edges.
The magnitude of the gradient G(x, y) of the image I(x, y) is defined as given in equation (6)
G(x, y) = [G2
X(x, y) +G2
Y(x, y)] 1/2
= [[āI(x, y)/ āx]2
+[āI(x, y)/ āy]2
]1/2
(6)
For the given input image an efficient gradient computation is applied to find the edges, i.e.,
smoothing, sharpening the image. The gradient components GX(x,y) and GY(x,y) can be obtained
by using the Prewit or Sobel operator as given in Fig 1. A more practical version of G(x, y) is its
approximation using absolute values as given in equation (7).
G(x, y) = | GX(x, y) | + | GY(x, y) | (7)
(a) (b)
Fig.1: Sobel masks used to compute gradient components along GX and GY
2.3. MORPHOLOGICAL WATERSHED
The watershed transform [13]-[16] is a well known segmentation method coming from the field
of mathematical morphology. Watershed algorithm for image segmentation on the grey levels of
neighbouring pixels, assumes that a hole is drilled in minimum of the surface, and water flows
from different positions into the hole. The hole is supposed to be a central pixel of a
neighbourhood. For the following 3x3 and 5x5 grey level masks, the watersheds are recognized
with a peak or highest level h>=20, and the Peak points are shown in Fig 2 and Fig 3.
4. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
12
Fig. 2 Example of Grey level watershed with height h>=20.
When we increase highest level (h) value ,water will fall from peak top points to bottom points
and creates a good watershed.
Fig. 3 Watershed of grey level Image with 5X5 mask
2.4. REGION GROWING
Based on the similarity constraints, Region Growing [17]-[18] method merges small pixel regions
or sub regions into larger regions. Identify the set of seed points in the image, and region
growing is applied to each seed pixel and is appending with adjacent pixels that have similar
properties like color or gray level value.
3. OBJECT BASED CONTRAST STRETCHING
The process of object based contrast stretching includes the pre-processing step by finding the
edges of an image, and then the threshold is applied on the gradient image which is given as
marker to the morphological watershed algorithm. The morphological watershed segments the
image into various objects. As the watershed algorithm results in over segmentation, the region
growing algorithm is applied for partitioning the image into two significant regions i.e. the
foreground and background regions. The contrast stretching is separately applied to each region
and after enhancement of each region, the regions are merged to form an enhanced image. The
complete process of object based contrast stretching is given in Fig 4.
3.1 Gradient Computation
For the given input image efficient edge detection algorithm is applied using Sobel operator.
Step 1: Apply Sobel filter to the input image to find edges.
ā¢ In this step apply gradient computation on the input image using GX and GY along X-direction
and Y-direction respectively.
5. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
13
ā¢ Now calculate G(x, y) using equation (7).
ā¢ The resultant image is a edge preserved image.
Algorithm: Gradient_Computation (I)
Input: I (original image)
Output: G (edge preserved image)
{
% Apply sobel mask along x-direction on image I
Gx(x, y) = | ( z7 + 2z8 + z9) ā ( z1 + 2z2 + z3) |
% Apply sobel mask along y-direction on image I
Gy(x, y) = | ( z3 + 2z6 + z9) ā ( z1 + 2z4 + z7) |
% Sum of the two filtered images is the edge preserved image.
G(x, y) ā| ( z7 + 2z8 + z9) ā ( z1 + 2z2 + z3) | + | ( z3 + 2z6 + z9) ā ( z1 + 2z4 + z7) |
}
START
IINPUT IMAGE
INTER-OBJECT STRETCHING INTRA-OBJECT STRETCHING
OBJECT APPROXIMATION IMAGE
IS M1 > M2
OBJECT ERROR IMAGE
OUTPUT IMAGE
STOP
Fig. 4 Flow chart of Proposed Method
YES NO
MORPHOLOGICAL WATERSHED
REGION MERGING
GRADIENT COMPUTATION
GRADIENT THRESHOLDING
FOREGROUND AND BACK GROUND
IMAGE IDENTIFICATION
6. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
14
Algorithm: Gradient_Computation (I)
Input: I (original image)
Output: G (edge preserved image) {
% Apply sobel mask along x-direction on image I
Gx(x, y) = | ( z7 + 2z8 + z9) ā ( z1 + 2z2 + z3) |
% Apply sobel mask along y-direction on image I
Gy(x, y) = | ( z3 + 2z6 + z9) ā ( z1 + 2z4 + z7) |
% Sum of the two filtered images is the edge preserved image.
G(x, y) ā| ( z7 + 2z8 + z9) ā ( z1 + 2z2 + z3) | + | ( z3 + 2z6 + z9) ā ( z1 + 2z4 + z7) |
}
3.2 Gradient Thresholding
The approach of gradient thresholding provides a simple and effective way to prevent over-
segmentation, by providing markers to watershed segmentation. The process is defined by
equation (8).
(8)
where Gth(x, y) is the threshold gradient magnitude, GT is a given threshold. The value of GT can
be determined based on the following condition.
GT(x,y)=(G į“ B)(x,y)+[G(x,y)ā(G į“ B)(x,y)] (9)
Where G į“ B is the opening operation performed on G by the disk shape structuring element B of
size 3*3.
For the given input image an efficient gradient thresholding is applied to find the improved
threshold image.
Step 2: Apply Gradient Thresholding to the Gradient Computation image to find improved
threshold image.
ā¢ In this step apply open operation to the Gradient Computation image.
ā¢ Now calculate GT(x, y) using equation (9).
ā¢ Now find the Gth(x, y) using equation (8), and the resultant image is an efficient gradient
threshold image.
Algorithm: Gradient_Thresholding (G)
Input: G (edge image)
7. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
15
Output: Gth (improved threshold image) {
% Apply open filter on image G
G ā B = Ī“B (ĪµB (G))
% Ī“B = erosion with B on G
% ĪµB = dilation with B on G
% Calculate the threshold GT
GT(x,y)=(G į“ B)(x,y)+[G(x,y)ā(G į“ B)(x,y)]
% Find the gradient thresholding Gth(x, y) which is the improved gradient threshold image
}
3.3 Segmentation Using Watersheds
Step 3: Apply Morphological Watershed to the improved gradient threshold image to get
the segmented image.
ā¢ This step applies watershed operation to the Gradient Thresholding image.
ā¢ The resultant image is a over segmented image.
Algorithm: Watershed_algorithm (Gth)
Input: Gth (Gradient thresholding image)
Output: W (segmentation image) {
% Apply watershed on image Gth
Apply watershed algorithm on Gth image by using 3*3 mask.
% The resultant image using watershed algorithm is an segmented image.
}
3.4 Region Growing
Step 4: Apply Region Growing to the watershed segmentation image.
ā¢ In this step apply Region Growing operation to the watershed segmentation image.
ā¢ The resultant image is a partitioned into two regions.
Algorithm: Region_Growing (W)
Input: W (watershed image)
Output: R (partitioned image) {
% Apply Region Growing on image W
Select a seed pixel and grow the region until terminating condition is reached.
% The resultant image using Region Growing is Region partitioned image.
}
3.5 FOREGROUND AND BACKGROUND IDENTIFICATION
After image segmentation the segmented regions featuring homogeneous intensity and bearing
contrast to their adjacent neighbours are extracted. These regions are treated as the objects that
constitute the image. In order to enhance the image at inter-object level and intra-object level
9. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
17
3.8 Object Contrast Stretching
The contrast stretching is applied on the image objects at inter object level and intra object level.
At inter object level an approach of stretching between adjacent local extremes is used to change
the local contrast between objects. At intra object level the uniform linear stretching is used to
enhance the textural features of objects while maintaining their homogeneity.
3.8.1 Inter Object Stretching
Step 8: In this step stretching between adjacent local extremes is used to change the local contrast
between objects.
Algorithm: Interobject_Stretching (Ia)
Input: Ia(object approximation image)
Output: Iā
a (Inter Object Stretched Image) {
% Calculate the Region Maximum and Region Minimum values from the input image
% Now calculate the Maximum and Minimum value along the ith
row and jth
column
If(Region Maximum > Maximum)
Replace each Maximum value with Region Maximum
If(Region Minimum < Minimum)
Replace each Minimum value with Region Minimum
}
3.8.2 Intra Object Stretching
Step 9: In this step the uniform linear stretching is used to enhance the textural features of objects
while maintaining their homogeneity.
Algorithm: Interobject_Stretching (IĪµ)
Input: IĪµ (Object Error Image)
Output: Iā
Īµ (Intra Object Stretched Image) {
% Calculate the Region Minimum value from the input image
% Now calculate the Minimum value along the ith
row and jth
column
If (Region Minimum < Minimum)
Replace each Minimum value with Region Minimum
}
3.9 Output Enhanced Image
Enhanced images of inter object stretching and intra-object stretching can be combined together
to reconstruct the final output enhanced image. Since the enhancement operation is directly
applied to the object, the final enhanced image does not suffer from ringing, blocking, or other
false contouring artifacts. Moreover,nose is not enhanced, since the uniform linear stretching is
applied to homogeneous regions.
Step 10: Algorithm for getting the output enhanced image by using inter-object stretching and
intra-object stretching images is given below.
10. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
18
Algorithm: Enhance_Output (Iā
a, Iā
Īµ )
Input: Iā
a (Inter Object Stretched Image), Iā
Īµ (Intra Object Stretched Image)
Output: Iā
(Enhanced Image){
% Calculate the Region Mean values from the inter object stretched image
% Now perform the following operation for image enhancement
If (Region belongs to inter object stretched)
Iā
= Iā
a + Inter Object Stretched Region mean
Else
Iā
= Iā
a - Inter Object Stretched Region mean
}
4. COMPARISON OF OBJECT-BASED CONTRAST STRETCHING WITH
EXISTING METHODS
We can compare different contrast enhancement techniques by using subjective and objective
assessment. The subjective assessment is an estimation of quality where there is no pre
established measure and is based only on the opinion of the evaluator. The quantitative measures
are used for objective assessment. However we have used entropy as the quantitative measures.
Entropy is a statistical measure of randomness that can be used to characterize the input image.
5.1 Subjective Assessment On Images
(a) (b) (c) (d)
(e) (f)
Fig 1: Results for Lena: (a) input Image (b) result of HE Method (c) result of power Law Transformation
Method (d) result of Logarithmic Transformation (e) result of LHE Method (f) result after applying
OBCS Method.
11. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
19
Fig.1 shows the Lena image and their resultant contrast enhancement versions. The various
methods are compared. Fig1 (a) demonstrates the original image. HE method enhances the
contrast which results in over illumination of the image. This situation is observed in Fig1 (b).
For Power Law Transformation, the foreground part of the image has improved but the hair of
Lena is not much improved which is shown in the Fig1(c). For Logarithmic Transformation, the
background as well as the foreground has over brightness look and is shown in Fig1 (d). An
image with improved contrast and without natural look is shown in Fig1 (e) in the case of Local
Histogram Equalization (LHE) method. The OBCS method preserves the brightness and also
improves the contrast, but the portions became more dark which is observed in Fig1 (f).
Fig2 shows the Photographer image and their corresponding contrast enhancement versions. The
various methods are compared. Fig2 (a) is the original image of photographer. HE method
enhances the image but results in over illumination as shown in Fig2 (b). By applying Power
Law Transformation method, the image is enhanced but a problem is observed in the background
portion as in Fig2(c). In Logarithmic Transformation Method, the foreground image is brightened
and background portion is damaged can be observed in Fig2 (d). In Local Histogram Equalization
method, the contrast is improved but brightness is not preserved. Hence the output image does not
give a natural look which is shown in Fig2 (e). The OBCS Method preserves the brightness and
also improves the contrast of the image but the dark portion became more dark. It is observed in
Fig2 (f).
(a) (b) (d) (d)
lost
(e) (f)
Fig 2: Results for Photographer: (a) Original input Image (b) result of HE Method (c) result of Power Law
Transformation Method (d) result of Logarithmic Transformation (e) result after applying LHE Method
(f) result after applying OBCS Method.
Fig3 demonstrates the original input image and their corresponding contrast enhancement
versions. The different methods are compared. Fig3 (a) demonstrates the vegetables image. Over
illumination and better utilization of dynamic range of the pixel values is observed in the case of
HE method which is shown in Fig3 (b). By applying Power Law, the foreground brightness of the
vegetables increased is shown in Fig3(c). Logarithmic Transformation increases both foreground
and background parts of the image is shown in Fig3 (d).Contrast of the image is increased but the
image missing its natural appearance by applying LHE method is shown in Fig 3(e). When
12. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
20
compared to other methods OBCS preserves the brightness and enhancing the contrast of image,
which is observed in fig3 (f).
(a) (b) (c) (d)
(e) (f)
Fig 3: Results for vegetables: (a) Original input Image (b) result of Image after applying HE Method (c)
result of Power Law Transformation Method (d) result after applying Logarithmic Transformation (e)
result of LHE Method (f) result after applying OBCS Method
(a) (b) (c) (d)
(e) (f)
Fig 4: Results for Boy: (a) Original input Image (b) Enhanced Image of HE Method (c) result after
applying Power Law Transformation Method (d) Enhanced Image of Logarithmic Transformation (e)
result after applying LHE Method (f) Enhanced Image after applying OBCS Method.
13. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
21
Fig4 demonstrates the original input image Boy and their corresponding contrast enhancement
versions. The various methods are compared. Fig4 (a) is the original image with good contrast.
HE method enhances the image but enhancement more than the required level which is shown in
Fig4 (b). By applying Power Law Transformation method, the image is darkening as in Fig4(c).
Logarithmic Transformation increases both the foreground and background which can be
observed in Fig4 (d).Local Histogram Equalization increases the contrast but brightness is not
preserved, hence the output image does not give a natural look which is shown in Fig4 (e). The
OBCS Method preserves the brightness and also improves the contrast of the image. It is
observed in Fig4 (f).
5.2 Objective Assessment On Images
Entropy is a statistical measure of randomness that can be used to characterize the input image.
Entropy is defined as
Entropy=sum (p.*log2 (p))
Where p contains the histogram counts returned from imhist.
The mean squared error of the estimator or predictor T(Y) for U is
MSE [T(Y); U] =E [(T(Y) ā U) 2
]
Table 1: Entropy Values of Different Images
METHOD LENA Photographer vegetables BOY
OBCS 5.9767 6.8357 6.6142 6.1258
HE 6.3265 6.7479 6.5276 5.9967
PLT 0.1248 0.0000 0.0000 0.0188
LT 0.3581 0.0310 0.0011 0.0188
LHE 7.7450 7.7024 7.8382 7.8826
Table 2: Mean Square Error Values of Different Images
METHOD LENA Photographer vegetables BOY
OBCS 0.0030 0.0075 0.0099 0.0072
HE 0.0148 0.0013 0.0030 0.0410
PLT 0.0563 0.1130 0.1275 0.0320
LT 0.1448 0.2734 0.3077 0.0886
LHE 0.0450 0.0010 0.0009 0.0556
5. CONCLUSION
The method is applied on various types of images. The assessment is performed by using both
subjective and objective evaluation. Objective analysis has been performed using MSE and
Entropy. It is seen that mean square error is less using OBCS method for all images. The
subjective analysis shows that image has been improved in all the cases, but it can be observed
that in the good contrast images the background is growing more darken which will be the future
work of this research.
14. International Journal of Advanced Information Technology (IJAIT) Vol. 4, No. 3, June 2014
22
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23
Authors
G.Srinivasa Rao graduated in B.E(CSE) from Marathwada university, India in the
year 1989, received masters degree M.Tech (CSE) from JNTU Hyderabad in the
year 2008, M.S degree in software systems from Birla Institute of Technology and
Science, pilani in 1996 and he is pursuing PhD from JNTUH, Hyderabad. He has 23
years of teaching Experience. Presently he is working as Associate Professor in the
department of Information Technology, RVR & JC College of Engineering, Guntur.
His research interest includes Image and Signal Processing, algorithms and web
technologies. He is a member of CSI.
Dr. A. Sri Krishna received the PhD degree from JNTUK, Kakinada in 2010, M.Tech
degree in Computer Science from Jawaharlal Nehru Technological University (JNTU)
in 2003, M.S degree in software systems from Birla Institute of Technology and
Science, Pilani in 1994, AMIE degree in Electronics & communication Engineering
from Institution of Engineers, Kolkata in 1990. She has 23 years of teaching experience
as Assistant Professor, Associate Professor, Professor and presently she is working as a
Professor and Head, Dept of Information Technology at RVR&JC College of
Engineering, Guntur. She has published 15 papers in International/ National Journals
and Conferences. Her research interest includes Image Processing and Pattern Recognition. She is member
of IE(I) and member of CSI.
Dr. S.Mahaboob Basha did his B.E.(Electronics) from Bangalore University, M.Tech.
from JNTU Anantapur and Ph.D. from S.K.University, Anantapur. He worked in
S.K.University in the Dept. Computer Science & Technology and in Wipro
Technologies, Bangalore. He has 21 years of teaching and 2 years of industrial
experience. He has 6 years of research experience. He is an expert in java technologies
and has delivered many guest lectures in various universities/colleges on Java, VC++,
Software Engineering, Data Structures and DBMS. His areas of interest in research are:
Image Compression, Image Filtering, Steganography, Network Security and Bio-Medical Engineering. He
has published 10 papers in reputed national and international journals. He is the life-time member of ISTE
CH.Jeevan Prakash did his B.Tech Graduation in Information Technology, in RVR & JC
College of Engineering, Chowdavaram, Guntur Andhra Pradesh, under ANU university
India. He has been actively participating and presenting papers in student technical
Symposium seminars at National Level. Her area of interest includes Image Processing,
web technologies.