java source code for
Automatic Image Equalization And Contrast Enhancement Using Gaussian Mixture
|
automatic image equalization and contrast enhancement using gaussian mixture modeling pdf
|
java source code for
Automatic Image Equalization And Contrast Enhancement Using Gaussian Mixture
25-08-2015, 02:38 PM
automatic image equalization and contrast enhancement using gaussian mixture modeling pdf
In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels’ gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.
Existing System:
Image enhancement is required mostly for better visualization or rendering of images to aid our visual perception. There are various reasons, why a raw image data requires processing before display. The dynamic range of the intensity values may be small due to the presence of strong background illumination, as well as due to the insufficient lighting. It may be the other way also. The dynamic range of the original image may be too large to be accommodated by limited number of bit-planes of a display device. The problem gets more complicated when the illumination of the scene widely varies in the space focused on the enhancement of gray-level images in the spatial domain. These methods include adaptive histogram equalization, unsharp masking, constant variance enhancement, homomorphic filtering, high-pass, and low-pass filtering, etc.
Proposed System:
We proposed automatic contrast enhancement of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms.
The proposed algorithm is free from parameter setting. Instead, the pixel values of an input image are modeled using the Gaussian mixture model (GMM). The intersection points of the Gaussian components are used in partitioning the dynamic range of the input image into input gray-level intervals. The gray levels of the pixels in each input interval are transformed according to the dominant Gaussian component and the CDF of the interval to obtain the contrast-equalized image.
We have proposed an automatic image enhancement algorithm that employs Gaussian mixture modeling of an input image to perform nonlinear data mapping for generating visually pleasing enhancement on different types of images. Performance comparisons with state-of-the-art techniques show that the proposed algorithm can achieve image equalization that is good enough even under diverse illumination conditions. The proposed algorithm can be applied to both gray-level and color images
In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels’ gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.
Existing System:
Image enhancement is required mostly for better visualization or rendering of images to aid our visual perception. There are various reasons, why a raw image data requires processing before display. The dynamic range of the intensity values may be small due to the presence of strong background illumination, as well as due to the insufficient lighting. It may be the other way also. The dynamic range of the original image may be too large to be accommodated by limited number of bit-planes of a display device. The problem gets more complicated when the illumination of the scene widely varies in the space focused on the enhancement of gray-level images in the spatial domain. These methods include adaptive histogram equalization, unsharp masking, constant variance enhancement, homomorphic filtering, high-pass, and low-pass filtering, etc.
Proposed System:
We proposed automatic contrast enhancement of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms.
The proposed algorithm is free from parameter setting. Instead, the pixel values of an input image are modeled using the Gaussian mixture model (GMM). The intersection points of the Gaussian components are used in partitioning the dynamic range of the input image into input gray-level intervals. The gray levels of the pixels in each input interval are transformed according to the dominant Gaussian component and the CDF of the interval to obtain the contrast-equalized image.
We have proposed an automatic image enhancement algorithm that employs Gaussian mixture modeling of an input image to perform nonlinear data mapping for generating visually pleasing enhancement on different types of images. Performance comparisons with state-of-the-art techniques show that the proposed algorithm can achieve image equalization that is good enough even under diverse illumination conditions. The proposed algorithm can be applied to both gray-level and color images
|
« Next Oldest | Next Newest »
|
