Improved local histogram equalization with gradient-based weighting process for edge preservation

This paper presents a novel local histogram equalization by combining the transformation functions of the non-overlapped sub-images based on the gradient information for edge preservation and better visualization. To ameliorate the problems of the over- and under-enhancement produced by conventional local histogram equalization, the bilateral Bezier curve-based histogram modification strategy is first employed to modify the significant and insufficient changes of each cumulative distribution in each sub-image. Yet, the gradient information has not been considered, and the cumulative distribution of some enhanced sub-images are still significant or insufficient because of the over- and under-enhancement, respectively. Therefore, the key insight of the proposed method is that the transformation functions of the partitioned sub-images will be weighed and combined based on the proportion of gradients to preserve the image texture. In addition, the input image is separated into the non-overlapped sub-images for reducing the time complexity. Based on the eight representative test images and mean opinion score, the experimental results demonstrate that the proposed method is quite competitive with four state-of-the-art histogram equalization methods in the literature. Furthermore, according to the subjective evaluation, it is observed that the proposed method can also apply to the practical applications and achieve good visual quality.