Exposure correction using deep learning

Jing Wang; Xiongfei Li; Zeyu Wang; Haoran Duan; Xiaoli Zhang

doi:10.1117/1.JEI.28.3.033003

13 May 2019 Exposure correction using deep learning

Jing Wang, Xiongfei Li, Zeyu Wang, Haoran Duan, Xiaoli Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 3, 033003 (May 2019). https://doi.org/10.1117/1.JEI.28.3.033003

Abstract

In the case of poor lighting conditions, it is easy to capture the underexposed images with low contrast and low quality. Traditional single-image enhancement methods often fail in revealing image details because of the limited information in a single-source image. A single underexposed image enhancement method based on adaptive decomposition and convolutional neural network (CNN) is proposed. The CNN training models only need images with different brightness rather than a strict ground-truth image. First, a simple effective synchronous decomposition method is proposed to solve the synergy problem in multisource image decomposition. Then, two CNN models are designed for the high-frequency part and low-frequency part, respectively. They process the high-frequency and low-frequency sub-bands, instead of the entire source images. The weight map obtained from the CNN model represents the contrast distribution. The exposure map generated by gradient-based visibility assessment indicates the exposure distribution. Finally, the weight map and the exposure map are multiplied to generate the final decision map. Experimental results demonstrate that the proposed method outperforms competing methods.

Citation Download Citation

Jing Wang, Xiongfei Li, Zeyu Wang, Haoran Duan, and Xiaoli Zhang "Exposure correction using deep learning," Journal of Electronic Imaging 28(3), 033003 (13 May 2019). https://doi.org/10.1117/1.JEI.28.3.033003

Received: 11 January 2019; Accepted: 17 April 2019; Published: 13 May 2019

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