  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 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. Code is
available at https://github.com/JianghaiSCU/Diffusion-Low-Light.

Computer Vision and Pattern Recognition