  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.
