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

• URL: http://arxiv.org/abs/2407.07056v2
• Date: Wed, 10 Jul 2024 11:25:26 GMT
• Title: CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement
• Authors: Wei Wang, Zhi Jin,
• Abstract summary: 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. 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.
• Score: 22.60541726111682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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