Recovering noise-covered details from low-light images is challenging, and the results given by previous methods leave room for improvement. Recent diffusion models show realistic and detailed image generation through a sequence of denoising refinements and motivate us to introduce them to low-light image enhancement for recovering realistic details. However, we found two problems when doing this, i.e., 1) diffusion models keep constant resolution in one reverse process, which limits the speed; 2) diffusion models sometimes result in global degradation (e.g., RGB shift). To address the above problems, this paper proposes a Pyramid Diffusion model (PyDiff) for low-light image enhancement. PyDiff uses a novel pyramid diffusion method to perform sampling in a pyramid resolution style (i.e., progressively increasing resolution in one reverse process). Pyramid diffusion makes PyDiff much faster than vanilla diffusion models and introduces no performance degradation. Furthermore, PyDiff uses a global corrector to alleviate the global degradation that may occur in the reverse process, significantly improving the performance and making the training of diffusion models easier with little additional computational consumption. Extensive experiments on popular benchmarks show that PyDiff achieves superior performance and efficiency. Moreover, PyDiff can generalize well to unseen noise and illumination distributions.

翻译：从低光照图像中恢复被噪声掩盖的细节极具挑战性，现有方法的效果仍有改进空间。近期扩散模型通过一系列去噪精炼过程实现了逼真且细节丰富的图像生成，这启发我们将其引入低光照图像增强领域以恢复真实细节。然而，我们发现在应用过程中存在两个问题：1）扩散模型在单次逆向过程中保持恒定分辨率，限制了处理速度；2）扩散模型有时会导致全局退化（如RGB偏移）。针对上述问题，本文提出一种用于低光照图像增强的金字塔扩散模型（PyDiff）。PyDiff采用新颖的金字塔扩散方法，以金字塔分辨率风格执行采样（即在单次逆向过程中逐步提升分辨率）。金字塔扩散使PyDiff相比传统扩散模型大幅提速，且不引入性能退化。此外，PyDiff使用全局校正器缓解逆向过程中可能出现的全局退化，显著提升性能的同时仅需极少的额外计算开销。在公开基准数据集上的大量实验表明，PyDiff实现了卓越的性能与效率。值得注意的是，PyDiff对未见过的噪声与光照分布具有良好的泛化能力。