CAIP2017 - 17th international Conference on Computer Analysis of Images and Patterns
A New Image Contrast Enhancement Algorithm using Exposure Fusion Framework

Zhenqiang Ying1, Ge Li1*, Yurui Ren2, Ronggang Wang1, Wenmin Wang1
1School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China
2Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

Source Code Available

Our Framework.

Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. Although many image enhancement techniques have been proposed to solve this problem, existing methods inevitably introduce contrast under- and over-enhancement. In this paper, we propose an exposure fusion framework and an enhancement algorithm to provide an accurate contrast enhancement. Specifically, we first design the weight matrix for image fusion using illumination estimation techniques. Then we introduce our camera response model to synthesize multi-exposure images. Next, we find the best exposure ratio so that the synthetic image is well-exposed in the regions where the original image under-exposed. Finally, the input image and the synthetic image are fused according to the weight matrix to obtain the enhancement result. Experiments show that our method can obtain results with less contrast and lightness distortion compared to that of several state-of-the-art methods.

Comparison Results (NASA Dataset)

Comparison Results (NPE Dataset)

This work was supported by the grant of National Science Foundation of China (No.U1611461), Shenzhen Peacock Plan (20130408-183003656), and Science and Technology Planning Project of Guangdong Province, China (No. 2014B090910001).