This is the official PyTorch implementation repository for Pre-trained Low-light Image Enhancement Transformer (LIET), an efficient Low-light image enhancement transformer.
Low-light image enhancement is a long-standing low-level vision problem because low-light images frequently have serious aesthetic quality flaws. Current low-light image enhancement methods based on deep neural networks have achieved impressive progress on this task. Unlike the mainstream CNN-based methods, we propose an effective low-light enhancement solution inspired by the Transformer that has demonstrated impressive performance in various tasks to address such problems. The key of this solution includes an image synthesis pipeline and a powerful Transformer-based pre-trained model named LIET. Precisely, the image synthesis pipeline consists of illumination simulation and realistic noise simulation, which can simulate more realistic low-light images to alleviate the bottleneck of data scarcity. LIET consists of a pair of streamlined CNN-based encoder- decoders and a Transformer body, which can effectively perform global/local contextual feature extraction with relatively low computational cost. We extensively evaluate the proposed approach through extensive experiments, and the results demonstrate that our solution is highly competitive with state-of-the-art methods. All the codes will be released soon.
Fig 1 Visual comparison with SOTA low-light image enhancement methods in outdoor scenes with large light ratios. Better with enlarged views to observe the results (same for the following figures).
Fig 2 Visual comparison with SOTA low-light image enhancement methods in dark indoor scenes.
Fig 3 Visual comparison of noise suppression effects with SOTA low-light image enhancement methods in night scenes with complex lighting.
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