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.

翻译：低照度图像增强（LLIE）技术随着手机摄影需求的激增而不断发展，然而现有方法大多忽视了压缩问题——这在资源受限的手机摄影中至关重要。多数LLIE方法忽略了这一因素，从而限制了其实际效能。本研究系统探究了JPEG压缩对低照度图像的影响，揭示了由于暗区普遍存在的低像素值导致JPEG压缩造成显著信息损失的问题。为此，我们提出了压缩感知预训练Transformer（CAPformer），采用创新的预训练策略从未压缩的低照度图像中学习无损信息。此外，所提出的亮度引导自注意力（BGSA）机制增强了合理信息聚合能力。实验证明，我们的方法在缓解压缩对LLIE的影响方面具有显著优势，展现了其在资源受限场景下提升低照度图像增强效果的潜力。