DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual Domains

• URL: http://arxiv.org/abs/2501.12235v1
• Date: Tue, 21 Jan 2025 15:58:16 GMT
• Title: DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual Domains
• Authors: Junyu Xia, Jiesong Bai, Yihang Dong,
• Abstract summary: Low-light image enhancement (LLE) aims to improve the visual quality of images captured in poorly lit conditions. These issues hinder the performance of computer vision tasks such as object detection, facial recognition, and autonomous driving. We propose the Dual Light Enhance Network (DLEN), a novel architecture that incorporates two distinct attention mechanisms.
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• Abstract: Low-light image enhancement (LLE) aims to improve the visual quality of images captured in poorly lit conditions, which often suffer from low brightness, low contrast, noise, and color distortions. These issues hinder the performance of computer vision tasks such as object detection, facial recognition, and autonomous driving.Traditional enhancement techniques, such as multi-scale fusion and histogram equalization, fail to preserve fine details and often struggle with maintaining the natural appearance of enhanced images under complex lighting conditions. Although the Retinex theory provides a foundation for image decomposition, it often amplifies noise, leading to suboptimal image quality. In this paper, we propose the Dual Light Enhance Network (DLEN), a novel architecture that incorporates two distinct attention mechanisms, considering both spatial and frequency domains. Our model introduces a learnable wavelet transform module in the illumination estimation phase, preserving high- and low-frequency components to enhance edge and texture details. Additionally, we design a dual-branch structure that leverages the power of the Transformer architecture to enhance both the illumination and structural components of the image.Through extensive experiments, our model outperforms state-of-the-art methods on standard benchmarks.Code is available here: https://github.com/LaLaLoXX/DLEN

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