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

• URL: http://arxiv.org/abs/2309.06941v3
• Date: Wed, 08 Jan 2025 09:35:58 GMT
• Title: DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision
• Authors: Xiangchen Yin, Zhenda Yu, Xin Gao, Xiao Sun,
• Abstract summary: We propose a DCT-driven enhancement transformer (DEFormer) framework to restore lost details in the dark area.<n>Our framework has achieved superior results on the LOL and MIT-Adobe FiveK datasets.
• Score: 12.150160523389957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement restores the colors and details of a single image and improves high-level visual tasks. However, restoring the lost details in the dark area is still a challenge relying only on the RGB domain. In this paper, we delve into frequency as a new clue into the model 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. Additionally, we propose a cross domain fusion (CDF) to reduce the differences between the RGB domain and the frequency domain. Our DEFormer has achieved superior results on the LOL and MIT-Adobe FiveK datasets, improving the dark detection performance.

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