Exact Histogram Specification Optimized for Structural Similarity

An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods. Index terms: histogram modification, histogram equalization, optimization for perceptual visual quality, structural similarity gradient ascent, histogram watermarking, contrast enhancement

New Stereo Vision Algorithm Composition Using Weighted Adaptive Histogram Equalization and Gamma Correction

This work presents the composition of a new algorithm for a stereo vision system to acquire accurate depth measurement from stereo correspondence. Stereo correspondence produced by matching is commonly affected by image noise such as illumination variation, blurry boundaries, and radiometric differences. The proposed algorithm introduces a pre-processing step based on the combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Gamma Correction Weighted Distribution (AGCWD) with a guided filter (GF). The cost value of the pre-processing step is determined in the matching cost step using the census transform (CT), which is followed by aggregation using the fixed-window and GF technique. A winner-takes-all (WTA) approach is employed to select the minimum disparity map value and final refinement using left-right consistency checking (LR) along with a weighted median filter (WMF) to remove outliers. The algorithm improved the accuracy 31.65% for all pixel errors and 23.35% for pixel errors in nonoccluded regions compared to several established algorithms on a Middlebury dataset

An Adaptive Approach for image Enhancement and Naturalness Preservation

Image enhancement simply means getting a clearer image, that is to process an image so that the result is more suitable than the original image for specific applications. The enhancement process does not increase the inherent information content in the data. But it does increase the dynamic range of the chosen features so that they can be detect easily. Image enhancement and Naturalness preservation plays an important role in image processing and analysis. Naturalness preservation while enhancing the details of an image is very essential to maintain a good perceptual quality. Many algorithms are available for image enhancement. This paper proposes an adaptive method for naturalness preservation and image enhancement. Image Enhancement using bi-log transformation and differential intensity histogram equalization are adaptively adopted to get the best results. First, a brightpass filter is defined to decompose the image into reflectance and illumination. The illumination image is processed and synthesis with reflectance image to obtain the enhanced image. Illumination image can be processed either by using bi-log transformation or differential intensity histogram equalization. Depending upon the cumulative density function of the illumination image, this method adaptively chooses bi-log transformation or histogram equalization for image enhancement. Lightness order error(LOE) is used to measure the naturalness preservation objectively. Lower the LOE value, better the naturalness preserved