Low‑light enhancement method with dual branch feature fusion and learnable regularized attention  

作　　者：Yixiang Sun Mengyao Ni Ming Zhao Zhenyu Yang Yuanlong Peng Danhua Cao 

机构地区：[1]School of Optical and Electronic Information,Huazhong University of Science and Technology,Wuhan 430074,China [2]State Grid Information&Telecommunication Branch,Beijing 100761,China

出　　处：《Frontiers of Optoelectronics》2024年第3期93-111,共19页光电子前沿（英文版）

基　　金：supported by State Grid Corporation of China(5700-202325308A-1-1-ZN);Information&Telecommunication Branch of State Grid Jiangxi Electric Power Company.

摘　　要：Restricted by the lighting conditions,the images captured at night tend to sufer from color aberration,noise,and other unfavorable factors,making it difcult for subsequent vision-based applications.To solve this problem,we propose a two-stage size-controllable low-light enhancement method,named Dual Fusion Enhancement Net(DFEN).The whole algorithm is built on a double U-Net structure,implementing brightness adjustment and detail revision respectively.A dual branch feature fusion module is adopted to enhance its ability of feature extraction and aggregation.We also design a learnable regularized attention module to balance the enhancement efect on diferent regions.Besides,we introduce a cosine training strategy to smooth the transition of the training target from the brightness adjustment stage to the detail revision stage during the training process.The proposed DFEN is tested on several low-light datasets,and the experimental results demonstrate that the algorithm achieves superior enhancement results with the similar parameters.It is worth noting that the lightest DFEN model reaches 11 FPS for image size of 1224×10^(24)in an RTX 3090 GPU.

关 键 词：Power inspection Low-light enhancement Feature fusion Learnable regularized attention 

分 类 号：TM73[电气工程—电力系统及自动化] TP391.41[自动化与计算机技术—计算机应用技术]