Fast mutual-information-based contrast enhancement
Gang Cao, Lifang Yu, Huawei Tian, Xianglin Huang, Yongbin Wang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Recently, T. Celik proposed an effective image contrast enhancement (CE) method based on spatial mutual information and PageRank (SMIRANK). According to the state-of-the-art evaluation criteria, it achieves the best visual enhancement quality among existing global CE methods. However, SMIRANK runs much slower than the other counterparts, such as histogram equalization (HE) and adaptive gamma correction. Low computational complexity is also required for good CE algorithms. In this paper, we novelly propose a fast SMIRANK algorithm, called FastSMIRANK. It integrates both spatial and gray-level downsampling into the generation of pixel value mapping function. Moreover, the computation of rank vectors is speeded up by replacing PageRank with a simple yet efficient row-based operation of mutual information matrix. Extensive experimental results show that the proposed FastSMIRANK could accelerate the processing speed of SMIRANK by about 20 times, and is even faster than HE. Comparable enhancement quality is preserved simultaneously.