EL2NM: Extremely Low-light Noise Modeling Through Diffusion Iteration

Jiahao Qin, Pinle Qin, Rui Chai, Jia Qin, Zanxia Jin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1085-1094

Low-light Original Denoising (LOD) is a challenging task in Computational Photography (CP). The low number of photons in low light environments makes imaging very difficult. The most difficult step in LOD is to establish a noise model under low light. Currently there are numerous approaches aim to noise modeling however the noise established have significant differences from real noise due to the highly intricate distribution of noise. Towards this goal this paper proposes an Extremely Low-light Noise Modeling (EL2NM) approach which designs an original image condition constraint module and a multi-noise fusion module to generate complex noise consistent with real scenes. In order to satisfy the complex noise distribution in low-light environments instead of just Gaussian noise we integrate various noises into cold diffusion to establish a realistic noise generation model for extremely low-light environments. At the same time to avoid the image semantic misinterpret during the reverse diffusion process we propose to use conditional image to guide noise generation of the diffusion model. Extensive experiments demonstrate that our proposed method EL2NM exhibits excellent performance in extremely low-light environments and achieves the state-of-the-art on Starlight Dataset.