Conventional contrast enhancement algorithms such as histogram equalization and adaptive gamma correction overemphasize images without preserving edges, lose details near edges or amplify noise. These are more obvious in case of enhancing dark images with non-uniform illumination, which contain both bright and dark regions. This is mainly because Histogram Equalization and adaptive gamma correction perform a global transformation considering only pixel brightness level.
Kim Thae Song, a section head at the Faculty of Information Science and Technology, has proposed a new algorithm to enhance dark image contrast with non-uniform illumination preserving edges.
His contributions are as follows.
He defined a new edge intensity histogram which represents local brightness variations for each brightness level. With the edge intensity histogram instead of the luminance histogram, adjusting dynamic range adaptively, he obtained a transformation function for efficient contrast enhancement which preserves edges, details and naturalness. To avoid noise amplification, he decomposed an input image into a base and detailed layer and calculated the edge intensity histogram for only the base layer. He enhanced only the base layer using the edge intensity histogram and combined it with the detailed layer which is linearly transformed.
He compared the performance of his method with existing methods such as HE, GMHE, EGEHE, AGCWD, AGCWHD and LTH using a set of dark images taken from the Caltech-256, NCEA, Kodak. To evaluate the proposed method, he used various metrics such as entropy, MSSIM, GMSD, EBCM and AMBE.
The experimental results showed that the proposed method is very effective for enhancing dark images with non-uniform illumination.
If further information is needed, please refer to his paper “An improved contrast enhancement for dark images with non-uniform illumination based on edge preservation” in “Multimedia Systems” (SCI).
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