Weight-Based Local Image Enhancement

Image enhancement ranges over numerous subjects. The most common application is digital photos taken in our daily life. Due to the different environmental conditions or the photographers, it frequently leads into too dark or bright images in local area. Either in certain Pattern Recognition(PR) system, by the way of pre-processing as known as image enhancement we could obtain more distinguishing features in order to classify them further precisely. As for some indoor surveillance systems, we fail to monitor efficiently because of the poor light source generating over dark areas in a few images. Thus, we will modify this through the technology of image enhancement.

Hence we wish that we could make some proper local enhancements as to meet the demands of naked-eyed observations without destroying the rest parts. Owing to the higher sensitivity of human beings to luminance and RGB color space is higher correlated not easy to process, we usually transfer images to YCbCr color space then do certain adjustments in accordance with the luminance. Through increasing the weight of low contrast area and GA optimization for solution space, we could attain the proper transfer curve for enhancing local low contrast images. Together with a use of Spatial Diffusion Filter processing the boundary problems arising from two different transfer curves, we can achieve the goal of local enhancement without destroying the quality of rest areas in the meanwhile.