International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document discusses digital image processing concepts including:
- Image acquisition and representation, including sampling and quantization of images. CCD arrays are commonly used in digital cameras to capture images as arrays of pixels.
- A simple image formation model where the intensity of a pixel is a function of illumination and reflectance at that point. Typical ranges of illumination and reflectance are provided.
- Image interpolation techniques like nearest neighbor, bilinear, and bicubic interpolation which are used to increase or decrease the number of pixels in a digital image. Examples of applying these techniques are shown.
- Basic relationships between pixels including adjacency, paths, regions, boundaries, and distance measures like Euclidean, city block, and
This document discusses pixel relationships and neighborhood concepts in digital images. It defines a pixel and pixel connectivity. There are different types of pixel neighborhoods, including 4-neighbor, 8-neighbor, and diagonal neighbors. Connected components are sets of pixels that are connected based on pixel adjacency. Algorithms can label connected components and identify distinct image regions. Various distance measures quantify how close pixels are, such as Euclidean, Manhattan, and chessboard distances. Arithmetic and logical operators can combine pixel values from different images. Neighborhood operations apply functions to pixels based on their values and those of nearby pixels.
Setting the lower order bit plane to zero would have the effect of reducing the number of distinct gray levels by half. This would cause the histogram to become more peaked, with more pixels concentrated in fewer bins.
1. Point operations modify pixel values without changing the image size or structure. Each new pixel value depends only on the original value at that point.
2. Histogram equalization finds a point operation to make the modified image histogram approximate a uniform distribution. This can only shift and merge histogram entries, not split them.
3. Other point operations discussed include increasing brightness by adding a constant value to each pixel, inverting pixels by subtracting from 255, thresholding to make pixels above a level white and below black, and alpha blending images with transparency control.
4. Automatic contrast adjustment and increasing contrast by multiplying pixel values by 1.5 are also point operations described.
This document discusses different techniques for image segmentation. It begins by defining image segmentation as dividing an image into regions based on similarity and differences between adjacent regions. The main approaches discussed are discontinuity-based segmentation, which looks for sudden changes in pixel intensity (edges), and similarity-based segmentation, which groups similar pixels into regions. The document then examines various methods for detecting edges, linking edges, thresholding, and region-based segmentation using techniques like region growing and splitting/merging.
Digital image enhancement involves modifying images to improve visual quality. It can be done in the spatial or frequency domain. Spatial domain enhancement works directly with pixel values. Key point processing techniques in the spatial domain include: digital negative, contrast stretching, thresholding, gray level slicing, bit plane slicing, and dynamic range compression. These techniques apply mathematical transforms to pixel values to darken or lighten regions of the image for better visualization.
This document discusses various mathematical tools used in digital image processing (DIP), including array versus matrix operations, linear versus nonlinear operations, arithmetic operations, set and logical operations, spatial operations, vector and matrix operations, and image transforms. Key points include:
- Array operations are performed on a pixel-by-pixel basis, while matrix operations consider relationships between pixels.
- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
- Spatial operations include single-pixel, neighborhood, and geometric transformations of pixel locations and intensities.
- Images can be represented as vectors and transformed using matrix operations.
- Common transforms like Fourier use separable, symmetric kernels to decompose images into frequency domains.
Adaptive Median Filters
Elements of visual perception
Representing Digital Images
Spatial and Intensity Resolution
cones and rods
Brightness Adaptation
Spatial and Intensity Resolution
This document discusses digital image processing concepts including:
- Image acquisition and representation, including sampling and quantization of images. CCD arrays are commonly used in digital cameras to capture images as arrays of pixels.
- A simple image formation model where the intensity of a pixel is a function of illumination and reflectance at that point. Typical ranges of illumination and reflectance are provided.
- Image interpolation techniques like nearest neighbor, bilinear, and bicubic interpolation which are used to increase or decrease the number of pixels in a digital image. Examples of applying these techniques are shown.
- Basic relationships between pixels including adjacency, paths, regions, boundaries, and distance measures like Euclidean, city block, and
This document discusses pixel relationships and neighborhood concepts in digital images. It defines a pixel and pixel connectivity. There are different types of pixel neighborhoods, including 4-neighbor, 8-neighbor, and diagonal neighbors. Connected components are sets of pixels that are connected based on pixel adjacency. Algorithms can label connected components and identify distinct image regions. Various distance measures quantify how close pixels are, such as Euclidean, Manhattan, and chessboard distances. Arithmetic and logical operators can combine pixel values from different images. Neighborhood operations apply functions to pixels based on their values and those of nearby pixels.
Setting the lower order bit plane to zero would have the effect of reducing the number of distinct gray levels by half. This would cause the histogram to become more peaked, with more pixels concentrated in fewer bins.
1. Point operations modify pixel values without changing the image size or structure. Each new pixel value depends only on the original value at that point.
2. Histogram equalization finds a point operation to make the modified image histogram approximate a uniform distribution. This can only shift and merge histogram entries, not split them.
3. Other point operations discussed include increasing brightness by adding a constant value to each pixel, inverting pixels by subtracting from 255, thresholding to make pixels above a level white and below black, and alpha blending images with transparency control.
4. Automatic contrast adjustment and increasing contrast by multiplying pixel values by 1.5 are also point operations described.
This document discusses different techniques for image segmentation. It begins by defining image segmentation as dividing an image into regions based on similarity and differences between adjacent regions. The main approaches discussed are discontinuity-based segmentation, which looks for sudden changes in pixel intensity (edges), and similarity-based segmentation, which groups similar pixels into regions. The document then examines various methods for detecting edges, linking edges, thresholding, and region-based segmentation using techniques like region growing and splitting/merging.
Digital image enhancement involves modifying images to improve visual quality. It can be done in the spatial or frequency domain. Spatial domain enhancement works directly with pixel values. Key point processing techniques in the spatial domain include: digital negative, contrast stretching, thresholding, gray level slicing, bit plane slicing, and dynamic range compression. These techniques apply mathematical transforms to pixel values to darken or lighten regions of the image for better visualization.
This document discusses various mathematical tools used in digital image processing (DIP), including array versus matrix operations, linear versus nonlinear operations, arithmetic operations, set and logical operations, spatial operations, vector and matrix operations, and image transforms. Key points include:
- Array operations are performed on a pixel-by-pixel basis, while matrix operations consider relationships between pixels.
- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
- Spatial operations include single-pixel, neighborhood, and geometric transformations of pixel locations and intensities.
- Images can be represented as vectors and transformed using matrix operations.
- Common transforms like Fourier use separable, symmetric kernels to decompose images into frequency domains.
Adaptive Median Filters
Elements of visual perception
Representing Digital Images
Spatial and Intensity Resolution
cones and rods
Brightness Adaptation
Spatial and Intensity Resolution
A Comparative Study of Histogram Equalization Based Image Enhancement Techniq...Shahbaz Alam
Four widely used histogram equalization techniques for image enhancement namely GHE, BBHE, DSIHE, RMSHE are discussed. Some basic definitions and notations are also attached. All analysis are done by using MATLAB . Pictures are taken from the book "Digital Image Processing" by Rafael C. Gonzalez and Richard E. Woods. The presentation slide was made for my B.Sc project purpose.
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSINGPriyanka Rathore
Image processing techniques can involve converting images to digital form and applying transformations like the Laplace transform. The Laplace transform is useful for applications like image sharpening, edge detection, and blob detection. It involves calculating the second derivative of the image to help identify edges and other discontinuities. The zero crossings of the Laplace transform output are particularly useful for edge detection as they indicate where the slope of the image changes most rapidly. While the Laplace transform provides benefits like simpler implementation and reliable noise performance, it can also result in spaghetti-like edge effects with complex computations.
Image Interpolation Techniques with Optical and Digital Zoom Conceptsmmjalbiaty
Digital image concepts and interpolation techniques for optical and digital zoom are discussed. There are three main types of interpolation used for resizing images: nearest neighbor, bilinear, and bicubic. Nearest neighbor is the simplest but produces the lowest quality, while bicubic is the most complex but highest quality. Optical zoom uses lens magnification before sensing, whereas digital zoom interpolates after sensing, resulting in lower quality than optical zoom. Interpolation methods assign pixel values to new locations during resizing based on weighting patterns around the original pixel values.
The document discusses various image enhancement techniques in the spatial domain. It covers basic gray level transformations like negatives, log transformations, and power law transformations. It also discusses histogram processing and enhancement using arithmetic operations. Furthermore, it explains smoothing and sharpening spatial filters, and how to combine different spatial enhancement methods. The document provides examples and background on these fundamental image enhancement concepts.
This document discusses different methods of image segmentation: thresholding, edge-based segmentation, and region-based segmentation. It provides details on various thresholding techniques including basic global thresholding, Otsu's method, multiple thresholding, and variable thresholding. For edge-based segmentation, it mentions basic edge detection, the Marr-Hildreth edge detector, and watersheds. Finally, it covers region-based segmentation and provides an algorithm for region growing.
The document discusses image restoration and reconstruction techniques. It covers topics like image restoration models, noise models, spatial filtering, inverse filtering, Wiener filtering, Fourier slice theorem, computed tomography principles, Radon transform, and filtered backprojection reconstruction. As an example, it derives the analytical expression for the projection of a circular object using the Radon transform, showing that the projection is independent of angle and equals 2Ar√(r2-ρ2) when ρ ≤ r.
This document contains 80 questions related to digital signal and image processing. The questions cover topics such as image transforms, filters, noise, compression, segmentation, and more. Justification is required for some questions, while others involve calculations, derivations or explanations of key concepts. The questions vary in difficulty and mark allocation from 5 to 10 marks. They also specify the exam or year in which the question appeared previously.
Image Interpolation Techniques with Optical and Digital Zoom Concepts -semina...mmjalbiaty
full details about Spatial and Intensity Resolution , optical and digital zoom concepts and the common three interpolation algorithms for implementing zoom in image processing
This document discusses various algorithms used for computer graphics rendering including scan conversion, line drawing, circle drawing, ellipse drawing, and polygon filling. It describes the Digital Differential Analyzer (DDA) algorithm for line drawing and Bresenham's algorithm as an improvement over DDA. Circle drawing is achieved using the midpoint circle algorithm and ellipse drawing using the midpoint ellipse algorithm. Polygon filling can be done using scan line filling or boundary filling algorithms.
3.point operation and histogram based image enhancementmukesh bhardwaj
The document discusses various techniques for digital image enhancement, including point operations, histogram equalization, and frequency domain methods. Point operations directly map input pixel values to output values using functions like contrast stretching and clipping. Histogram equalization maps values to equalize the image histogram for better contrast. Frequency methods like unsharp masking and homomorphic filtering enhance images in the frequency domain by modifying high and low frequency components. The techniques can be used to improve images for applications in digital photography, iris recognition, microscopy, and entertainment.
Digital Image Processing covers intensity transformations that can be performed on images. These include basic transformations like negatives, log transformations, and power-law transformations. It also discusses image histograms, which measure the frequency of each intensity level in an image. Histogram equalization aims to improve contrast by mapping intensities to produce a uniform histogram. It works by spreading out the most frequent intensity values.
This document presents a new color image segmentation approach based on overlap wavelet transform (OWT). OWT extracts wavelet features to better separate different patterns in an image. The proposed method also uses morphological operators and 2D histogram clustering for effective segmentation. It is concluded that the proposed OWT method improves segmentation quality, is reliable, fast and computationally less complex than direct histogram clustering. When tested on various color spaces, the proposed segmentation scheme produced better results in RGB color space compared to others. The main advantages are its use of a single parameter and faster speed.
This document discusses image processing and digital images. It covers topics like:
- How images can be represented as functions that map pixel locations to intensity values.
- The process of sampling and quantizing to convert a continuous image into a digital image represented as a matrix of integer values.
- Different types of image processing operations including point processing that transform pixel values independently and neighborhood processing that considers pixel locations.
- Specific point processing techniques like negative, log transformations, and gamma correction.
- Image enhancement methods like contrast stretching, histograms, and histogram equalization.
The document discusses image interpolation and its use in data hiding. It begins by explaining common interpolation methods like nearest neighbor, bilinear, and bicubic interpolation. It then proposes using neighbor mean interpolation for data hiding. The secret data is embedded in the interpolated image by modifying pixel values in 2x2 blocks. During extraction, the secret bits are recovered from differences between pixel values. An improved method is also presented where pixels are grouped to increase embedding capacity before modifying for data hiding.
Digital image processing using matlab: basic transformations, filters and ope...thanh nguyen
This document provides code solutions in Matlab for image processing homework assignments. It includes code to perform:
1. Basic grayscale transformations like negative, log, power-law, and piecewise linear on various images.
2. Histogram processing techniques like equalization and subtraction on images.
3. Smoothing and sharpening filters like averaging, median, Laplacian, and Sobel gradient filters to reduce noise and enhance edges.
4. Detailed explanations and examples are given for each transformation and filtering technique along with input and output images. The code utilizes various Matlab functions to perform the image processing tasks in a concise manner.
This document provides an overview of mathematical morphology and its applications to image processing. Some key points:
- Mathematical morphology uses concepts from set theory and uses structuring elements to probe and extract image properties. It provides tools for tasks like noise removal, thinning, and shape analysis.
- Basic operations include erosion, dilation, opening, and closing. Erosion shrinks objects while dilation expands them. Opening and closing combine these to smooth contours or fill gaps.
- Hit-or-miss transforms allow detecting specific shapes. Skeletonization reduces objects to 1-pixel wide representations.
- Morphological operations can be applied to binary or grayscale images. Structuring elements are used to specify the neighborhood of pixels
Histograms show the distribution of pixel intensities in an image by counting the number of pixels for each intensity value. Normalized histograms provide an estimate of the probability of each intensity occurring. Histogram equalization transforms the pixel intensity distribution of an image to a uniform distribution in order to increase contrast. It does this by using the cumulative distribution function to map intensities to new output values. Local histogram equalization performs this on neighborhoods within an image to enhance local details. Arithmetic and logical operations can also be used for image enhancement, such as AND, OR, and subtraction between images on a pixel-by-pixel basis.
This document discusses image enhancement techniques in the spatial domain. It describes two categories of spatial domain operations: point processing and neighborhood processing. Point processing involves direct manipulation of pixel values through techniques like contrast stretching and thresholding. Neighborhood processing considers pixels in a local region and applies techniques like averaging filters. The document outlines several gray level transformations for enhancement, including logarithmic, power-law, piecewise linear, and bit-plane slicing transformations. It also discusses arithmetic and logic operations on images.
This document provides information about a digital image processing lecture given by Dr. Moe Moe Myint from Technological University in Kyaukse, Myanmar. It includes the lecture schedule and contact information for Dr. Myint. The document also provides an overview of Chapter 2 which discusses elements of visual perception, light and the electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, and basic relationships between pixels. It provides examples of different types of digital images including intensity, RGB, binary, and index images. It also discusses the effects of spatial and intensity level resolution on images.
El resumen del documento es:
1) Un científico necesita formar un grupo de 20 criaturas de 5 razas diferentes de un planeta para ayudarlo en sus investigaciones.
2) Cada raza tiene diferentes habilidades y requerimientos de alimentos y climas.
3) Se debe formular un modelo de programación entera para seleccionar las razas que minimicen los costos de alimentos y preservantes durante un viaje de 10 meses, cumpliendo con los requisitos de habilidades, climas, alimentos y restricciones de las razas.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A Comparative Study of Histogram Equalization Based Image Enhancement Techniq...Shahbaz Alam
Four widely used histogram equalization techniques for image enhancement namely GHE, BBHE, DSIHE, RMSHE are discussed. Some basic definitions and notations are also attached. All analysis are done by using MATLAB . Pictures are taken from the book "Digital Image Processing" by Rafael C. Gonzalez and Richard E. Woods. The presentation slide was made for my B.Sc project purpose.
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSINGPriyanka Rathore
Image processing techniques can involve converting images to digital form and applying transformations like the Laplace transform. The Laplace transform is useful for applications like image sharpening, edge detection, and blob detection. It involves calculating the second derivative of the image to help identify edges and other discontinuities. The zero crossings of the Laplace transform output are particularly useful for edge detection as they indicate where the slope of the image changes most rapidly. While the Laplace transform provides benefits like simpler implementation and reliable noise performance, it can also result in spaghetti-like edge effects with complex computations.
Image Interpolation Techniques with Optical and Digital Zoom Conceptsmmjalbiaty
Digital image concepts and interpolation techniques for optical and digital zoom are discussed. There are three main types of interpolation used for resizing images: nearest neighbor, bilinear, and bicubic. Nearest neighbor is the simplest but produces the lowest quality, while bicubic is the most complex but highest quality. Optical zoom uses lens magnification before sensing, whereas digital zoom interpolates after sensing, resulting in lower quality than optical zoom. Interpolation methods assign pixel values to new locations during resizing based on weighting patterns around the original pixel values.
The document discusses various image enhancement techniques in the spatial domain. It covers basic gray level transformations like negatives, log transformations, and power law transformations. It also discusses histogram processing and enhancement using arithmetic operations. Furthermore, it explains smoothing and sharpening spatial filters, and how to combine different spatial enhancement methods. The document provides examples and background on these fundamental image enhancement concepts.
This document discusses different methods of image segmentation: thresholding, edge-based segmentation, and region-based segmentation. It provides details on various thresholding techniques including basic global thresholding, Otsu's method, multiple thresholding, and variable thresholding. For edge-based segmentation, it mentions basic edge detection, the Marr-Hildreth edge detector, and watersheds. Finally, it covers region-based segmentation and provides an algorithm for region growing.
The document discusses image restoration and reconstruction techniques. It covers topics like image restoration models, noise models, spatial filtering, inverse filtering, Wiener filtering, Fourier slice theorem, computed tomography principles, Radon transform, and filtered backprojection reconstruction. As an example, it derives the analytical expression for the projection of a circular object using the Radon transform, showing that the projection is independent of angle and equals 2Ar√(r2-ρ2) when ρ ≤ r.
This document contains 80 questions related to digital signal and image processing. The questions cover topics such as image transforms, filters, noise, compression, segmentation, and more. Justification is required for some questions, while others involve calculations, derivations or explanations of key concepts. The questions vary in difficulty and mark allocation from 5 to 10 marks. They also specify the exam or year in which the question appeared previously.
Image Interpolation Techniques with Optical and Digital Zoom Concepts -semina...mmjalbiaty
full details about Spatial and Intensity Resolution , optical and digital zoom concepts and the common three interpolation algorithms for implementing zoom in image processing
This document discusses various algorithms used for computer graphics rendering including scan conversion, line drawing, circle drawing, ellipse drawing, and polygon filling. It describes the Digital Differential Analyzer (DDA) algorithm for line drawing and Bresenham's algorithm as an improvement over DDA. Circle drawing is achieved using the midpoint circle algorithm and ellipse drawing using the midpoint ellipse algorithm. Polygon filling can be done using scan line filling or boundary filling algorithms.
3.point operation and histogram based image enhancementmukesh bhardwaj
The document discusses various techniques for digital image enhancement, including point operations, histogram equalization, and frequency domain methods. Point operations directly map input pixel values to output values using functions like contrast stretching and clipping. Histogram equalization maps values to equalize the image histogram for better contrast. Frequency methods like unsharp masking and homomorphic filtering enhance images in the frequency domain by modifying high and low frequency components. The techniques can be used to improve images for applications in digital photography, iris recognition, microscopy, and entertainment.
Digital Image Processing covers intensity transformations that can be performed on images. These include basic transformations like negatives, log transformations, and power-law transformations. It also discusses image histograms, which measure the frequency of each intensity level in an image. Histogram equalization aims to improve contrast by mapping intensities to produce a uniform histogram. It works by spreading out the most frequent intensity values.
This document presents a new color image segmentation approach based on overlap wavelet transform (OWT). OWT extracts wavelet features to better separate different patterns in an image. The proposed method also uses morphological operators and 2D histogram clustering for effective segmentation. It is concluded that the proposed OWT method improves segmentation quality, is reliable, fast and computationally less complex than direct histogram clustering. When tested on various color spaces, the proposed segmentation scheme produced better results in RGB color space compared to others. The main advantages are its use of a single parameter and faster speed.
This document discusses image processing and digital images. It covers topics like:
- How images can be represented as functions that map pixel locations to intensity values.
- The process of sampling and quantizing to convert a continuous image into a digital image represented as a matrix of integer values.
- Different types of image processing operations including point processing that transform pixel values independently and neighborhood processing that considers pixel locations.
- Specific point processing techniques like negative, log transformations, and gamma correction.
- Image enhancement methods like contrast stretching, histograms, and histogram equalization.
The document discusses image interpolation and its use in data hiding. It begins by explaining common interpolation methods like nearest neighbor, bilinear, and bicubic interpolation. It then proposes using neighbor mean interpolation for data hiding. The secret data is embedded in the interpolated image by modifying pixel values in 2x2 blocks. During extraction, the secret bits are recovered from differences between pixel values. An improved method is also presented where pixels are grouped to increase embedding capacity before modifying for data hiding.
Digital image processing using matlab: basic transformations, filters and ope...thanh nguyen
This document provides code solutions in Matlab for image processing homework assignments. It includes code to perform:
1. Basic grayscale transformations like negative, log, power-law, and piecewise linear on various images.
2. Histogram processing techniques like equalization and subtraction on images.
3. Smoothing and sharpening filters like averaging, median, Laplacian, and Sobel gradient filters to reduce noise and enhance edges.
4. Detailed explanations and examples are given for each transformation and filtering technique along with input and output images. The code utilizes various Matlab functions to perform the image processing tasks in a concise manner.
This document provides an overview of mathematical morphology and its applications to image processing. Some key points:
- Mathematical morphology uses concepts from set theory and uses structuring elements to probe and extract image properties. It provides tools for tasks like noise removal, thinning, and shape analysis.
- Basic operations include erosion, dilation, opening, and closing. Erosion shrinks objects while dilation expands them. Opening and closing combine these to smooth contours or fill gaps.
- Hit-or-miss transforms allow detecting specific shapes. Skeletonization reduces objects to 1-pixel wide representations.
- Morphological operations can be applied to binary or grayscale images. Structuring elements are used to specify the neighborhood of pixels
Histograms show the distribution of pixel intensities in an image by counting the number of pixels for each intensity value. Normalized histograms provide an estimate of the probability of each intensity occurring. Histogram equalization transforms the pixel intensity distribution of an image to a uniform distribution in order to increase contrast. It does this by using the cumulative distribution function to map intensities to new output values. Local histogram equalization performs this on neighborhoods within an image to enhance local details. Arithmetic and logical operations can also be used for image enhancement, such as AND, OR, and subtraction between images on a pixel-by-pixel basis.
This document discusses image enhancement techniques in the spatial domain. It describes two categories of spatial domain operations: point processing and neighborhood processing. Point processing involves direct manipulation of pixel values through techniques like contrast stretching and thresholding. Neighborhood processing considers pixels in a local region and applies techniques like averaging filters. The document outlines several gray level transformations for enhancement, including logarithmic, power-law, piecewise linear, and bit-plane slicing transformations. It also discusses arithmetic and logic operations on images.
This document provides information about a digital image processing lecture given by Dr. Moe Moe Myint from Technological University in Kyaukse, Myanmar. It includes the lecture schedule and contact information for Dr. Myint. The document also provides an overview of Chapter 2 which discusses elements of visual perception, light and the electromagnetic spectrum, image sensing and acquisition, image sampling and quantization, and basic relationships between pixels. It provides examples of different types of digital images including intensity, RGB, binary, and index images. It also discusses the effects of spatial and intensity level resolution on images.
El resumen del documento es:
1) Un científico necesita formar un grupo de 20 criaturas de 5 razas diferentes de un planeta para ayudarlo en sus investigaciones.
2) Cada raza tiene diferentes habilidades y requerimientos de alimentos y climas.
3) Se debe formular un modelo de programación entera para seleccionar las razas que minimicen los costos de alimentos y preservantes durante un viaje de 10 meses, cumpliendo con los requisitos de habilidades, climas, alimentos y restricciones de las razas.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
The document discusses using the XMPP protocol to enable communication between volunteer cloud entities and commercial clouds.
It proposes an XMPP-based messaging middleware architecture that would allow volunteer cloud components to exchange XML messages to coordinate tasks and share presence information. This would help address challenges like one-directional communication, latency, availability and security.
The architecture is described through two scenarios: 1) how volunteer cloud entities could internally communicate using XMPP, and 2) how clients could interact with a volunteer cloud to consume or contribute resources via XMPP connections between client and cloud servers.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Computational Engineering Research(IJCER)ijceronline
The document describes a wide band W-shaped microstrip patch antenna with an inverted U-slotted ground plane designed for wireless applications. A parametric study was performed by varying the feed location and return loss variations were observed. An impedance bandwidth of 24.5% was obtained. Simulation results for return loss, VSWR, radiation patterns, and gain satisfied theoretical conditions. The antenna design achieves wide bandwidth suitable for wireless applications like WLAN and Bluetooth.
Study on Contrast Enhancement with the help of Associate Regions Histogram Eq...IJSRD
Histogram equalization is an uncomplicated and extensively used image distinction enhancement technique. The crucial drawback of histogram equalization is it transforms the brightness of the image. To overcome this drawback, different histogram Equalization methods have been projected. These methods protect the brightness on the result image but, do not have a usual look. Therefore this paper is an attempt to bridge the gap and results after the processed Associate regions are collected into one image. The mock-up result explains that the algorithm can not only improve image information successfully but also remain the imaginative image luminance well enough to make it likely to be used in video arrangement directly.
Spatial domain filtering and intensity transformations are techniques used in image processing. Spatial domain refers to the pixels that make up an image. Spatial domain techniques operate directly on pixels by applying operators to pixels and their neighbors. Common operators include averaging, median filtering, and contrast adjustments. Spatial filtering techniques include smoothing to reduce noise and sharpening to enhance edges through differentiation. Intensity transformations map input pixel values to output values using functions like logarithms, power laws, and piecewise linear approximations to modify image contrast and highlight certain intensity ranges.
The document reports on the results of three image processing projects. The first project implemented Lloyd-Max quantization to reduce image file sizes and Retinex theory to compensate for uneven illumination. The second project used principal component analysis to compute eigenfaces for face recognition. The third project performed linear discriminant analysis and tensor-based linear discriminant analysis for binary classification and visual object recognition. Illumination compensation subtracted an estimated illumination plane from image intensities to reduce shadows. Eigenfaces were the principal components of a training set of face images. Tensor-based linear discriminant analysis treated images as higher-order tensors to outperform conventional LDA.
3 intensity transformations and spatial filtering slidesBHAGYAPRASADBUGGE
This document discusses basics of intensity transformations and spatial filtering of digital images. It covers the following key points:
- Intensity transformations map input pixel intensities to output intensities using an operator T. Common transformations include log, power-law, and piecewise-linear functions.
- Spatial filters operate on neighborhoods of pixels. Linear filters perform averaging or correlation while non-linear filters use ordering like median.
- Basic filters include smoothing to reduce noise, sharpening to enhance edges using Laplacian or unsharp masking, and gradient for edge detection.
- Fuzzy set theory can be applied to intensity transformations by defining membership functions for concepts like dark/bright. It can also be used for spatial filtering by defining
Perimetric Complexity of Binary Digital ImagesRSARANYADEVI
Perimetric complexity is a measure of the complexity of binary pictures. It is defined as the sum of inside and outside perimeters of the foreground, squared, divided by the foreground area, divided by . Difficulties arise when this definition is applied to digital images composed of binary pixels. In this article we identify these problems and propose solutions. Perimetric complexity is often used as a measure of visual complexity, in which case it should take into account the limited resolution of the visual system. We propose a measure of visual perimetric complexity that meets this requirement.
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International Journal of Computational Engineering Research(IJCER)
1. International Journal of Computational Engineering Research||Vol, 03||Issue, 10||
Enhancement of Sharpness and Contrast Using Adaptive
Parameters
1,
1,
Allabaksh Shaik , 2,Nandyala Ramanjulu,
Department of ECE , Priyadarshini Institute of Technology, Kanuparthipadu,Nellore-524004
Department of ECE , Sri Sai Institute of Technology & Science, Rayachoty,Kadapa District
2,
ABSTRACT
In the applications like medical radiography enhancing movie features and observing the
planets it is necessary to enhance the contrast and sharpness of an image. We propose a generalized
unsharp masking algorithm using the exploratory data model as a unified framework. The proposed
algorithm is designed to address three issues:1) simultaneously enhancing contrast and sharpness by
means of individual treatment of the model component and the residual,2)reducing the halo effect by
means of an edge-preserving filter, and 3)solving the out of range problem by means of log ratio and
tangent operations. We present a new system called the tangent system which is based upon a specific
bregman divergence. Experimental results show that the proposed algorithm is able to significantly
improve the contrast and sharpness of an image. Using this algorithm user can adjust the two
parameters the contrast and sharpness to have desired output
INDEX TERMS: Bregman divergence, exploratory data model, generalized linear system, image
enhancement, unsharp masking.
I.
INTRODUCTION
Now a days in digital image processing applications requires an image that contains detailed
information. So we need to convert a blurry image and undefined image into a sharper image. So in application
orientation we need to enhance the contrast and sharpness of the image. While enhancing the qualities of the
image, it is clear that noise components enhancement is undesirable. In this view the enhancement of undershoot
and overshoot creates the effect called as halo effect. So ideally our algorithm should only enhance the image
details. To overcome this problem we need to use filters that are not sensitive to noise and do not smooth sharp
edges. In this paper we use log ratio approach to overcome the out of range problem. In this paper we proposed
a generalized system which is used for general addition and multiplication which is shown in below figure.
x y =ø-1 [ø(x) + ø(y)]
(1)
And
α x = ø-1 [α ø (x)]
(2)
Where x and y are sample signals, α is a real scalar and ø is non-linear function. And the remarkable property of
log ratio approach is that it is operable in the region (0, 1) gray scale and can overcome the out of range
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2. Enhancement Of Sharpness And Contrast…
problem. The following are the main issues in contrast enhancement and sharpness enhancement in current
existing systems. The existing systems dealt these processes as two tasks which will increase the complexity.
The contrast enhancement does not lead to the sharpness enhancement. Many of the
existing systems facing the problem of halo effect. While enhancing the sharpness of an image, in parallel the
noise of the image also is increased. Though all the systems have enhanced the sharpness and contrast of the
image, this will give the best result only after careful rescaling process. This was not there in existing system.
These issues can be solved through our approach using exploratory data model and log ratio approach.
II. EXPLORATORY DATA ANALYSIS MODEL FOR IMAGE ENHANCEMENT
A well known idea in exploratory data analysis is to decompose a signal into two parts. From This
point of view, the output of the filtering process, denoted y = f(x), can be regarded as the part of the image that
fits the Model. Thus,
x=y d
(3)
Where d is called as detail signal (the residual) and defined as d = x
y where Ꮎ is generalized subtraction.
The general form of unshaped masking algorithm is as follows.
V = h(y)
g(d)
(4)
Where v is the output and h(y) and g(d) are the linear or non-linear functions. Explicitly we can say that the
image model that is being sharpened is the residual. In addition, this model permits the incorporation of contrast
enhancement by means of a suitable processing function h(y) such as adaptive histogram equalization. As such,
the generalized algorithm can enhance the overall contrast and sharpness of the image.
Fig 2. Generalized unsharp masking algorithm block diagram
The following is the comparison between classical unsharp masking algorithm and generalized unsharp masking
algorithm.
y
d
h(y)
UM
LPF
x-y
Y
GUM
EPF
x y
g(d)
ACE
Output
v
y+g(d)
Rescale
Yes
h(y)
g(d)
No
We address the issue of the halo effect by using an edge-preserving filter-the IMF to generate the signal. The
choice of the IMF is due to its relative simplicity and well studied properties such as the root signals. We
address the issue of the need for a careful rescaling process by using new operations defined according to the
log-ratio and new generalized linear system. Since the gray scale set is closed under these new operations We
address the issue of contrast enhancement and sharpening by using two different processes. The image y is
processed by adaptive histogram equalization and the output is called h(y). The detail image is processed by
g(d)=
where
is the adaptive gain and is a function of the amplitude of the detail signal d. The final
output of the algorithm is then given by
v = h(y)
[
]
(5)
III. LOG-RATIO, GENERALIZED LINEAR SYSTEMS AND BREGMAN DIVERGENCE
The new generalized operations will be defined using the equations (1) and (2). Here these operations
are defined based on the view vector space used logarithmic image processing.
A. definitions and properties of log-ratio operations:
Nonlinear Function: We consider the pixel gray scale x (0, 1) of an image. For an N-bit image, we can first add
a very small positive constant to the pixel gray value then scale it by 2 -N such that it is in the range (0, 1). The
nonlinear function is defined as follows:
(6)
To simplify notation, we define the ratio of the negative image to the original image as follows:
X=
(7)
||Issn 2250-3005 ||
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3. Enhancement Of Sharpness And Contrast…
Using equation (1) the addition of two gray scales x1 and x2 can be defined as
x1 x2 =
=
(8)
And the multiplication of the gray scale x by a real scalar
x=
is defined using (2) as follows
(9)
This operation is called as scalar multiplication and we define a new zero scale denoted by e as follows
e x=x
(10)
The value of
can be defined as follows.
(11)
Negative image and subtraction operation:
The negative of the gray scale, denoted by, is obtained by solving
x
= 1/2
(12)
Now we can define the subtraction operation using the addition operation defined in (8) as follows.
x1 Ꮎ x2 = x1 (Ꮎ x2)
=
=
(13)
Properties:
Now when positive value a is added to the gray scale x then there will be fluctuations in output image y based
on the values of a given below.
Order of reflections of the log-ratio addition.
0< x a < min( x,a)
a<x a<x
x<x
a<a
Min (x,a) < x a <1
Order of reflections of the log-ratio multiplication.
0<
0<x<½
1/2 < x < 1
>x
In case of log-ratio addition, based on the value of the constant „a‟ the variation of output gray value y is shown
in the above table. The variations in y depends on the positive and negative nature of the gray scale x and the
constant „a‟. While coming to log-ratio multiplication the result will be based on the gain value and the
polarity of the gray scale x.
Computation:
Computations can be directly performed using the new operations. For example, for any real numbers
and , the weighted summation is given by
(
(
)=
(14)
the generalized weighted averaging operation is defined as
y=(
(
) (
=
………
(
)
(15)
1/N
1/N
Where G = (
)
and =(
)
are the weighted geometric means of the original and the
negative images, respectively.
An indirect computational method is through the nonlinear function (6).
y=
(16)
B. Log-Ratio, the Generalized Linear System and the Bregman Divergence:
We study the connection between the log-ratio and the Bregman divergence.
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4. Enhancement Of Sharpness And Contrast…
1) Log-Ratio and the Bregman divergence:
The Bregman divergence of two vectors x and y, denoted D F(X
, is defined as follows:
DF
= F(X) – F(Y) – (X-Y)T
(17)
Where F:
is a strictly convex and differentiable function defined over an open convex domain and
is the gradient of F evaluated at point y. the weighted left-sided centroid is given by
CL = arg
=
(
(18)
Comparing equations (17) and (19) we can see that
is scalar and we can conclude as follows.
F(x) =
= -x log (x) – (1-x) log (1-x)
(19)
Where the constant of the indefinite integral is omitted. The previous function F(x) is called the bit entropy and
the corresponding Bregman divergence is defined as
(20)
This is called as logistic loss. Therefore, the log-ratio has an intrinsic connection with the Bregman divergence
through the generalized weighted average. This connection reveals a geometrical property of the log-ratio which
uses a particular Bregman divergence to measure the generalized distance between two points. This can be
compared with the classical weighted average which uses the Euclidean distance. In terms of the loss function,
while the log-ratio approach uses the logistic loss function, the classical weighted average uses the squared loss
function.
2) Generalized Linear Systems and the Bregman Divergence:
we can derive the connections between the bregman divergence with other established generalized linear
systems such as the MHS with
where
and the LIP model
where
. the corresponding bregman divergences are the kullback-Leibler(KL) for MHS
(21)
And the LIP
(22)
Respectively.
3) A new generalized system:
We can define a new generalized system by letting
. This approach of defining a generalized
linear system is based upon using the Bregman divergence as a measure of the distance of two signal samples.
The measure can be related to the geometrical properties of the signal space. A new generalized linear system
for solving the out-of-range problem can be developed by using the following Bregman divergence
(23)
Which is generated by the convex function
whose domain is (1,-1). The nonlinear unction
for
the corresponding generalized linear system is as follows:
(24)
In this paper the generalized system is called as tangent system and the scalar addition and multiplication
(equation (1) and (2)) is called as tangent operations.
In image processing applications the pixel values from the interval [0,2 N) is mapped into (1,-1).then the image is
processed using tangent operations and then the image is mapped to [0,2 N) as reversible operation. The total
signal set is confined to (-1,1) and hence using this the out-of –range problem can overcome using log-ratio. We
can define the negative image and the subtraction operation, and study the order relations for the tangent
operations.
IV. PROPOSED ALGORITHM
A. Dealing with Color Images
We first convert a color image from the RGB color space to the HSI or the LAB color space. The
chrominance components such as the H and S components are not processed. After the luminance component is
processed, the inverse conversion is performed. An enhanced color image in its RGB color space is obtained.
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5. Enhancement Of Sharpness And Contrast…
The rationale for only processing the luminance component is to avoid a potential problem of altering the white
balance of the image when the RGB components are processed individually.
B. Enhancement of the Detail Signal
1) The Root Signal and the Detail Signal: Let us denote the Median filtering operation as a function
which maps the input to the output. An IMF operation can be represented as:
where
is the iteration index and
. The signal is usually called the root signal of the filtering
process if
. It is convenient to define the root signal as follows.
n = min k,
subject to
Where
is the suitable measure between the two images and is a user defined threshold.
It can be easily seen that the definition of the root signal depends upon the threshold.
2) Adaptive Gain Control: We can see from Fig. 3 that to enhance the detail signal the gain must be greater
than one. Using a universal gain for the whole image does not lead to good results, because to enhance the small
details a relatively large gain is required. However, a large gain can lead to the saturation of the detailed signal
whose values are larger than a certain threshold. Saturation is undesirable because different amplitudes of the
detail signal are mapped to the same amplitude of either 1 or 0.This leads to loss of information. Therefore, the
gain must be adaptively controlled. In the following, we only describe the gain control algorithm for using with
the log-ratio operations. Similar algorithm can be easily developed for using with the tangent operations. To
Control the gain; we first perform a linear mapping of the detail signal d to a new signal c
(30)
Such that the dynamic range of c is (-1, 1). A simple idea is to set the gain as a function of the signal c and to
gradually decrease the gain from its maximum value
when ׀c < T to its minimum
value when ׀c.1→׀
More specifically, we propose the following adaptive gain control function
(31)
Where is a parameter that controls the rate of decreasing. The two parameters α and β are obtained by solving
the equations:
γ (0) =
and γ (1) =
. For a fixed ή, we can easily determine the two parameters as follows:
(32)
And
(33)
Although both
and
could be chosen based upon each individual image processing task, in general it
is reasonable to set
. This setting follows the intuition that when the amplitude of the detailed signal is
large enough, it does not need further amplification. For example, we can see that
=
(34)
As such, the scalar multiplication has little effect. We now study the effect of and
by setting
.
C. Contrast Enhancement of the Root Signal
For contrast enhancement, we use adaptive histogram equalization implemented by a Matlab function in the
Image Processing Toolbox. The function, called “adapthisteq,” has a parameter controlling the contrast. This
parameter is determined by the user through experiments to obtain the most visually pleasing result. In our
simulations, we use default values for other parameters of the function.
V. RESULTS AND COMPARISON
We first show the effects of the two contributing parts: contrast enhancement and detail enhancement.
Contrast enhancement by adaptive Histogram equalization does remove the haze-like effect of the original
image and contrast of the cloud is also greatly enhanced. Next, we study the impact of the shape of the filter
mask of the median filter. For comparison we also show the result of replacing the median filter with a linear
filter having a uniform mask. As we can observe from these results, the use of a linear filter leads to the halo
effect which appears as a bright line surrounding the relatively dark mountains (for Using a median filter, the
halo effect is mostly avoided, although for the square and diagonal cross mask there are still a number of spots
||Issn 2250-3005 ||
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Page 43
6. Enhancement Of Sharpness And Contrast…
with very mild halo effects. However, the result from the horizontal-vertical cross mask is almost free of any
halo effect. In order to completely remove the halo effect, adaptive filter mask selection could be implemented:
the horizontal- vertical cross mask for strong vertical/horizontal edge, the diagonal cross mask for strong
diagonal edge and the square mask for the rest of the image. However, in practical application, it may be
sufficient to use a fixed mask for the whole image to reduce the computational time. We have also performed
experiments by replacing the log ratio operations with the tangent operations and keeping the same parameter
settings. We observed that there is no visually significant difference between the results.
The above shown graphs are the main differnces between the classical and generalized unsharp masking
algorithm. Here we show the clear difference between the addition and generalized addition. And at last we
show that the enhancement is more in generalized unsharp masking algorithm rather than in classical unsharp
masking algorithm.
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Page 44
8. Enhancement Of Sharpness And Contrast…
The above are the two results which processed using generalized unsharp masking algorithm. These two output
images show that the sharpness and contrast get enhanced in an exponential manner. No rescaling process is
used here. And the out of range problem is not encountered here since the range of gray scale is (0, 1).
REFERENCES:
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