AnlightenDiff: Anchoring diffusion probabilistic model on low light image enhancement
Author(s)
Author(s)
Chan, Y.-H.
Date Issued
2024
Publisher
IEEE
Journal
IEEE Transactions on Image Processing
Volume
33
Start page
6324
End page
6339
Abstract
Low-light image enhancement aims to improve the visual quality of images captured under poor illumination. However, enhancing low-light images often introduces image artifacts, color bias, and low SNR. In this work, we propose AnlightenDiff, an anchoring diffusion model for low light image enhancement. Diffusion models can enhance the low light image to well-exposed image by iterative refinement, but require anchoring to ensure that enhanced results remain faithful to the input. We propose a Dynamical Regulated Diffusion Anchoring mechanism and Sampler to anchor the enhancement process. We also propose a Diffusion Feature Perceptual Loss tailored for diffusion based model to utilize different loss functions in image domain. AnlightenDiff demonstrates the effect of diffusion models for low-light enhancement and achieving high perceptual quality results. Our techniques show a promising future direction for applying diffusion models to image enhancement.
SFU Affiliated Publication
Yes
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