Gaussian mixture model for Image Contrast Enhancement
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The objective of an image enhancement technique is to bring out hidden image details or to increase the contrast of an image with a low dynamic range. Such a technique produces an output image that subjectively looks better than the original image by increasing the gray-level differences (i.e., the contrast) among objects and background. Numerous enhancement techniques have been introduced, and these can be divided into three groups: 1) Techniques that decompose an image into high- and low-frequency signals for manipulation; 2) Transform-based techniques; and 3) Histogram modification techniqu...
The objective of an image enhancement technique is to bring out hidden image details or to increase the contrast of an image with a low dynamic range. Such a technique produces an output image that subjectively looks better than the original image by increasing the gray-level differences (i.e., the contrast) among objects and background. Numerous enhancement techniques have been introduced, and these can be divided into three groups: 1) Techniques that decompose an image into high- and low-frequency signals for manipulation; 2) Transform-based techniques; and 3) Histogram modification techniques. Techniques in the first two groups often use multiscale analysis to decompose the image into different frequency bands and enhance its desired global and local frequencies. These techniques are computationally complex but enable global and local contrast enhancement simultaneously by transforming the signals in the appropriate bands or scales. Furthermore, they require appropriate parameter settings that might otherwise result in image degradations. For example, the center-surround Retinex algorithm was developed to attain lightness and color constancy for machine vision applications.
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