Contrast Enhancement by Multi-Level Histogram Shape Segmentation with Adaptive Detail Enhancement for Noise Suppression

The usual problems associated with image enhancement include over- and under-enhancement, halo effects at edges and the degradation of the signal-to-noise ratio as the enhancement of details increases. Some of those problems manifest in the background and some in the details of the enhanced image. Our proposed method is to apply different techniques to enhance the background and details separately. For enhancement of the image background, a novel multi-level histogram shape segmentation method which will detect abrupt changes in the histogram is proposed so that regions of intensity values with a similar frequency of occurrence are segmented for individual equalization to avoid over-enhancement. For detail enhancement, a novel adaptive median based enhancement method is applied to the details to avoid over- and under-enhancement while suppressing noise by limiting the degree of enhancement in homogeneous regions. Halo effects due to the over-enhancement of edges are avoided in our proposed method by using an edge preserving filter for the separation of the background and details so that edges are excluded from detail enhancement. It has been shown that our proposed method is able to avoid the usual adverse problems of image enhancement while producing adequate overall enhancement.