Xiwen Liang¹, Xiaoning Yan², Nenghua Xu², Xiaoyan Chen¹, Hao Feng^1
1Tianjin University of Science and Technology, No. 1038 Dagu Nanlu, Hexi District, Tianjin, China, 300222
²Shenzhen softsz co. ltd, Shenzhen, China
pp. 316–320
ABSTRACT
Low illumination image enhancement is a difficult but scientific task. With the image brightness increasing, the noises are amplified, and with the contrast and detail increasing, the false information is generated. To solve this problem, a multi-branch attention network is proposed to process low-light images directly without additional operations. The proposed network is composed with enhancement module (EM) and Convolutional Block Attention Module (CBAM). The attention module can make the CNN network structure gradually focus on the weak light area in the image, and the enhancement module can fully highlight the multi-branch feature graph under the guidance of attention. In this way, the overall quality of the picture will be greatly improved, including contrast, brightness, etc. Through a large number of experiments, our model can produce better visual effects, and also achieve good results in quantitative indicators.

ARTICLE INFO
Article History
Received 25 November 2021
Accepted 19 October 2022

Keywords
Low-light image enhancement
Deep learning
Multi-branch fusion Convolutional neural network

JRNAL9402

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