CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement

Wei Wang; Zhi Jin

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arXiv · 2407.07056

CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement

Wei Wang·Zhi Jin

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Abstract

Low-Light Image Enhancement (LLIE) has advanced with the surge in phone photography demand, yet many existing methods neglect compression, a crucial concern for resource-constrained phone photography. Most LLIE methods overlook this, hindering their effectiveness. In this study, we investigate the effects of JPEG compression on low-light images and reveal substantial information loss caused by JPEG due to widespread low pixel values in dark areas. Hence, we propose the Compression-Aware Pre-trained Transformer (CAPformer), employing a novel pre-training strategy to learn lossless information from uncompressed low-light images. Additionally, the proposed Brightness-Guided Self-Attention (BGSA) mechanism enhances rational information gathering. Experiments demonstrate the superiority of our approach in mitigating compression effects on LLIE, showcasing its potential for improving LLIE in resource-constrained scenarios.

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BibTeX RIS

Wei Wang, Zhi Jin. 2024-07-09. CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement. https://arxiv.org/abs/2407.07056

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