DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision

Low-light image enhancement restores colors and details of single image and improves high-level visual tasks. However, restoring the lost details in the dark area is a challenge by only relying on the RGB domain. In this paper, we introduce frequency as a new clue into the network and propose a DCT-...

Full description

Saved in:
Bibliographic Details
Main Authors Yin, Xiangchen, Yu, Zhenda, Gao, Xin, Sun, Xiao
Format Journal Article
LanguageEnglish
Published 13.09.2023
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computer Vision and Pattern Recognition
Online AccessGet full text

Cover

More Information
Summary:Low-light image enhancement restores colors and details of single image and improves high-level visual tasks. However, restoring the lost details in the dark area is a challenge by only relying on the RGB domain. In this paper, we introduce frequency as a new clue into the network and propose a DCT-driven enhancement transformer (DEFormer) framework. First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE) to represent frequency features. In addition, we propose a cross domain fusion (CDF) for reducing the differences between the RGB domain and the frequency domain. Our DEFormer has achieved advanced results in both the LOL and MIT-Adobe FiveK datasets and improved the performance of dark detection.
DOI:10.48550/arxiv.2309.06941