KANDiff: Low-light Image Enhancement Based on Diffusion Models

Low-light image enhancement technique aims to improve the contrast and brightness of low-light images. Diffusion models, attributed to adeptness at capturing intricate details, have achieved good results in image enhancement, but there are problems such as inadequate estimation of noise characteristics and the emergence of color bias in the enhanced images. To address the aforementioned problems, this paper proposes a diffusion models-based method for low-light image enhancement, termed KANDiff. In the diffusion model architecture, this paper adds the nonlinear learnable activation function Kolmogorov–Arnold network to the noise estimation network U-Net to generate higher quality enhanced images. Additionally, KANDiff mitigates color bias in the enhanced images through a joint loss function and employs a patch-based image restoration strategy to significantly enhance the model generalization capability. The experimental results show that the KANDiff algorithm proposed in this paper can achieve high-quality image enhancement and achieve better enhancement effects compared to other algorithms.