Structure-guided Diffusion Transformer for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2504.15054v1
• Date: Mon, 21 Apr 2025 12:30:01 GMT
• Title: Structure-guided Diffusion Transformer for Low-Light Image Enhancement
• Authors: Xiangchen Yin, Zhenda Yu, Longtao Jiang, Xin Gao, Xiao Sun, Zhi Liu, Xun Yang,
• Abstract summary: We introduce DiT into the low-light enhancement task and design a novel Structure-guided Diffusion Transformer based Low-light image enhancement framework.<n>We compress the feature through wavelet transform to improve the inference efficiency of the model and capture the multi-directional frequency band.<n>In addition, we propose a Structure-guided Attention Block (SAB) to pay more attention to texture-riched tokens and avoid interference from noisy areas in noise prediction.
• Score: 19.90700077104533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While the diffusion transformer (DiT) has become a focal point of interest in recent years, its application in low-light image enhancement remains a blank area for exploration. Current methods recover the details from low-light images while inevitably amplifying the noise in images, resulting in poor visual quality. In this paper, we firstly introduce DiT into the low-light enhancement task and design a novel Structure-guided Diffusion Transformer based Low-light image enhancement (SDTL) framework. We compress the feature through wavelet transform to improve the inference efficiency of the model and capture the multi-directional frequency band. Then we propose a Structure Enhancement Module (SEM) that uses structural prior to enhance the texture and leverages an adaptive fusion strategy to achieve more accurate enhancement effect. In Addition, we propose a Structure-guided Attention Block (SAB) to pay more attention to texture-riched tokens and avoid interference from noisy areas in noise prediction. Extensive qualitative and quantitative experiments demonstrate that our method achieves SOTA performance on several popular datasets, validating the effectiveness of SDTL in improving image quality and the potential of DiT in low-light enhancement tasks.

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