Logarithmic transform coefficient histogram matching with spatial equalization

Abstract

In this paper we propose an image enhancement algorithm that is based on utilizing histogram data gathered from transform domain coefficients that will improve on the limitations of the histogram equalization method. Traditionally, classical histogram equalization has had some problems due to its inherent dynamic range expansion. Many images with data tightly clustered around certain intensity values can be over enhanced by standard histogram equalization, leading to artifacts and overall tonal change of the image. In the transform domain, one has control over subtle image properties such as low and high frequency content with their respective magnitudes and phases. However, due to the nature of many of these transforms, the coefficient's histograms may be so tightly packed that distinguishing them from one another may be impossible. By placing the transform coefficients in the logarithmic transform domain, it is easy to see the difference between different quality levels of images based upon their logarithmic transform coefficient histograms. Our results demonstrate that combing the spatial method of histogram equalization with logarithmic transform domain coefficient histograms achieves a much more balanced enhancement, that out performs classical histogram equalization.

Comparative study of logarithmic enhancement algorithms with performance measure

“…The second of these is logarithmic transform histogram matching, or LTCHM. This involves modifying the logarithmic transform histogram of the original image to approximate the logarithmic transform histogram of the original image processed using histogram equalization [17].…”

Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

“…For example, is there a way of somehow combining histogram equalization and transform enhancement? The answer: Yes, using transform histograms [9], [10], which have, as of yet, been mostly unexplored. This paper explores three new methods for which transform histograms can be utilized for contrast enhancement of images: logarithmic transform histogram mapping, logarithmic transform histogram shifting, and logarithmic transform histogram shaping.…”

Parametric rational unsharp masking for image enhancement

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