Degrade for upgrade: Learning degradation representations for real-world low-light image enhancement-Reference-Cited by-同舟云学术

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Degrade for upgrade: Learning degradation representations for real-world low-light image enhancement

Published:2024-11 Issue: Volume:119 Page:109622
ISSN:0045-7906
Container-title:Computers and Electrical Engineering
language:en
Short-container-title:Computers and Electrical Engineering

Author:

Xu Lintao,Hu Changhui^ORCID,Zhu Weihong,Wu Fei,Cai Ziyun,Ye Mengjun,Lu Xiaobo

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference63 articles.

1. Adaptive enhanced swin transformer with U-net for remote sensing image segmentation;Gu;Comput Electr Eng,2022

2. Detection of highway lane lines and drivable regions based on dynamic image enhancement algorithm under unfavorable vision;Mingzhou;Comput Electr Eng,2021

3. Heatmap and edge guidance network for salient object detection;Zhang;Comput Electr Eng,2023

4. Low-light image and video enhancement using deep learning: A survey;Li;IEEE Trans Pattern Anal Mach Intell,2021

5. Benchmarking low-light image enhancement and beyond;Liu;Int J Comput Vis,2021

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