Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models

• URL: http://arxiv.org/abs/2401.17629v1
• Date: Wed, 31 Jan 2024 07:11:01 GMT
• Title: Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models
• Authors: Kyungsung Lee, Donggyu Lee, Myungjoo Kang
• Abstract summary: We propose SaFaRI, a spatial-and-frequency-aware diffusion model for Image Restoration (IR) Our model encourages images to preserve data-fidelity in both the spatial and frequency domains, resulting in enhanced reconstruction quality. Our thorough evaluation demonstrates that SaFaRI achieves state-of-the-art performance on both the ImageNet datasets and FFHQ datasets.
• Score: 7.947387272047602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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