Currently, the low-light image enhancement methods mainly adopt a single feature to reconstruct the target image. Among these methods, stacked upsampling-downsampling operations inevitably cause irreversible loss of high-frequency information when performing feature scaling, ultimately resulting in blurred detailed information in the enhanced image. To address this issue, this paper proposed a low-light image detail enhancement method based on edge feature guidance. The method consisted of three components: an edge feature extract module (EFEM), an enhancement module, and an edge-aware feature guidance module (EFGM). By leveraging Transformer and guided by edge features, it progressively generated high-quality enhanced images in a coarse-to-fine manner. First, the EFEM acquired edge features from low-light images via a parallel window transformer block (PWTB), which guided the image enhancement process. Second, the enhancement module employed a coarse-to-fine transformer block (CFTB), which included a channel transformer block (CTB) and a PWTB. These two components extracted global coarse-grained features and local fine-grained features respectively, and modifications were made to the feed-forward network (FFN) in the Transformer. Finally, the EFGM embedded edge features into the image feature space, mitigating the severe loss of details in dark regions. The experimental results show that the proposed method achieves peak signal-to-noise ratio (PSNR) of 24.97 dB, 23.20 dB, and 25.92 dB, and structural similarity index measure (SSIM) of 0.873, 0.865, and 0.941 on the LOL-v1, LOL-v2-real, and LOLv2-synthetic datasets, respectively. All these metrics outperform those of the current mainstream methods. In terms of subjective quality, the enhanced images well preserve the image detail information.
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