CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement
<|reference_start|>CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement: 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.<|reference_end|>
@article{wang2024capformer:,
    title={CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image
  Enhancement},
    author={Wei Wang and Zhi Jin},
    journal={arXiv preprint arXiv:2407.07056},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07056},
    primaryClass={cs.CV eess.IV}
}