Back Projection Generative Strategy for Low and Normal Light Image Pairs With Enhanced Statistical Fidelity and Diversity

Cheuk-Yiu Chan, Wan-Chi Siu, Yuk-Hee Chan, H. Anthony Chan

Abstract: Low light image enhancement (LLIE) using supervised deep learning is limited by the scarcity of matched low/normal light image pairs. We propose Back Projection Normal-to-Low Diffusion Model (N2LDiff-BP), a novel diffusion-based generative model that realistically transforms normal-light images into diverse low-light counterparts. By injecting noise perturbations over multiple timesteps, our model synthesizes low-light images with authentic noise, blur, and color distortions. We introduce innovative architectural components - Back Projection Attention, BP2 Feedforward, and BP Transformer Blocks - that integrate back projection to model the narrow dynamic range and nuanced noise of real low-light images. Experiment and results show N2LDiff-BP significantly outperforms prior augmentation techniques, enabling effective data augmentation for robust LLIE. We also introduce LOL-Diff, a large-scale synthetic low-light dataset. Our novel framework, architectural innovations, and dataset advance deep learning for low-light vision tasks by addressing data scarcity. N2LDiff-BP establishes a new state-of-the-art in realistic low-light image synthesis for LLIE.

External IDs:dblp:journals/tce/ChanSCC25