Back projection generative strategy for low and normal light image pairs with enhanced statistical fidelity and diversity
Author(s)
Author(s)
Chan, Y.-H.
Date Issued
2024
Publisher
IEEE
Journal
IEEE Transactions on Consumer Electronics
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, BP 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.
SFU Affiliated Publication
Yes
Availability at SFU Library
No database links found.