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Proceedings Paper

Fast mutual-information-based contrast enhancement
Author(s): Gang Cao; Lifang Yu; Huawei Tian; Xianglin Huang; Yongbin Wang
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Paper Abstract

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.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202L (21 July 2017);
Show Author Affiliations
Gang Cao, Communication Univ. of China (China)
Lifang Yu, Beijing Institute of Graphic Communication (China)
Huawei Tian, People’s Public Security Univ. of China (China)
Xianglin Huang, Communication Univ. of China (China)
Yongbin Wang, Communication Univ. of China (China)

Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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