AnlightenDiff: Anchoring Diffusion Probabilistic Model on Low Light Image Enhancement.

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

AnlightenDiff:低照度图像增强的锚定扩散概率模型
低照度图像增强的目的是改善在低照度条件下拍摄的图像的视觉质量。然而,增强弱光图像往往会带来图像伪影、色彩偏差和低信噪比。在这项工作中,我们提出了用于弱光图像增强的锚定扩散模型 AnlightenDiff。扩散模型可以通过迭代细化将弱光图像增强为曝光良好的图像,但需要锚定来确保增强结果忠实于输入图像。我们提出了一种动态调节扩散锚定机制和采样器来锚定增强过程。我们还提出了一种为基于扩散的模型量身定制的扩散特征感知损失,以利用图像域中的不同损失函数。AnlightenDiff 演示了扩散模型在弱光增强中的效果,并获得了高感知质量的结果。我们的技术为将扩散模型应用于图像增强指明了一个大有可为的未来方向。
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