Diffusion models have recently emerged as a promising framework for Image Restoration (IR), owing to their ability to produce high-quality reconstructions and their compatibility with established methods. Existing methods for solving noisy inverse problems in IR, considers the pixel-wise data-fidelity. In this paper, we propose SaFaRI, a spatial-and-frequency-aware diffusion model for IR with Gaussian noise. Our model encourages images to preserve data-fidelity in both the spatial and frequency domains, resulting in enhanced reconstruction quality. We comprehensively evaluate the performance of our model on a variety of noisy inverse problems, including inpainting, denoising, and super-resolution. Our thorough evaluation demonstrates that SaFaRI achieves state-of-the-art performance on both the ImageNet datasets and FFHQ datasets, outperforming existing zero-shot IR methods in terms of LPIPS and FID metrics.

翻译：近年来，扩散模型凭借其生成高质量重建结果的能力以及对现有方法的兼容性，已成为图像恢复领域极具前景的框架。现有用于解决含噪声逆问题的图像恢复方法通常仅考虑逐像素的数据保真度。本文提出了一种面向高斯噪声图像恢复的空间与频率感知扩散模型SaFaRI。该模型通过引导图像在空间域和频率域同时保持数据保真度，有效提升了重建质量。我们在涵盖图像修复、去噪和超分辨率等多种含噪声逆问题上全面评估了模型性能。实验结果表明，SaFaRI在ImageNet数据集和FFHQ数据集上均达到了最优性能，其在LPIPS和FID指标上全面超越了现有零样本图像恢复方法。