Abstract
Blind image restoration remains a significant challenge in low-level vision tasks. Recently, denoising diffusion models have shown remarkable performance in image synthesis. Guided diffusion models, leveraging the potent generative priors of pre-trained models along with a differential guidance loss, have achieved promising results in blind image restoration. However, these models typically consider data consistency solely in the spatial domain, often resulting in distorted image content. In this paper, we propose a novel frequency-aware guidance loss that can be integrated into various diffusion models in a plug-and-play manner. Our proposed guidance loss, based on 2D discrete wavelet transform, simultaneously enforces content consistency in both the spatial and frequency domains. Experimental results demonstrate the effectiveness of our method in three blind restoration tasks: blind image deblurring, imaging through turbulence, and blind restoration for multiple degradations. Notably, our method achieves a significant improvement in PSNR score, with a remarkable enhancement of 3.72\,dB in image deblurring. Moreover, our method exhibits superior capability in generating images with rich details and reduced distortion, leading to the best visual quality.
Abstract (translated)
盲图像恢复在低级视觉任务中仍是一个重大的挑战。最近，去噪扩散模型在图像合成方面表现出了卓越的性能。引导扩散模型利用预训练模型的强大生成先验以及微分引导损失，在盲图像恢复上取得了有希望的结果。然而，这些模型通常仅在空间域考虑数据一致性，常常导致图像内容扭曲。本文中，我们提出了一种新型的频谱感知引导损失，可以以即插即用的方式集成到各种扩散模型中。我们的引导损失基于2D离散小波变换，在空间和频率域同时强制执行内容一致性。实验结果展示了我们在三种盲恢复任务中的方法有效性：盲图像去模糊、湍流成像以及针对多种退化的盲恢复。值得注意的是，我们的方法在PSNR得分上实现了显著的提升，在图像去模糊方面提高了3.72 dB。此外，我们提出的方法在生成细节丰富且失真减少的图像方面表现出色，从而达到最佳视觉质量。
URL
https://arxiv.org/abs/2411.12450
