Diffusion models have achieved promising results in image restoration tasks,
yet suffer from time-consuming, excessive computational resource consumption,
and unstable restoration. To address these issues, we propose a robust and
efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
Specifically, we present a wavelet-based conditional diffusion model (WCDM)
that leverages the generative power of diffusion models to produce results with
satisfactory perceptual fidelity. Additionally, it also takes advantage of the
strengths of wavelet transformation to greatly accelerate inference and reduce
computational resource usage without sacrificing information. To avoid chaotic
content and diversity, we perform both forward diffusion and reverse denoising
in the training phase of WCDM, enabling the model to achieve stable denoising
and reduce randomness during inference. Moreover, we further design a
high-frequency restoration module (HFRM) that utilizes the vertical and
horizontal details of the image to complement the diagonal information for
better fine-grained restoration. Extensive experiments on publicly available
real-world benchmarks demonstrate that our method outperforms the existing
state-of-the-art methods both quantitatively and visually, and it achieves
remarkable improvements in efficiency compared to previous diffusion-based
methods. In addition, we empirically show that the application for low-light
face detection also reveals the latent practical values of our method.

我们提出了一种名为 DiffLL 的稳健和高效的基于扩散模型的低光图像增强方法，它利用波浪变换加速推理并降低计算资源使用但不损失信息，并通过前向扩散和反向去噪实现稳定去噪和减少随机性。我们还设计了高频率恢复模块，利用图像的垂直和水平细节来补充对角信息以实现更好的细粒度恢复。大量实验证明，我们的方法在定量和视觉上均优于现有的最先进方法，并且与以前的扩散方法相比，在效率上获得了显着的提高。此外，我们还经验性地证明了方法在低光人脸检测方面的潜在实际价值。