Invert2Restore: Zero-Shot Degradation-Blind Image Restoration

• URL: http://arxiv.org/abs/2503.21486v1
• Date: Thu, 27 Mar 2025 13:22:40 GMT
• Title: Invert2Restore: Zero-Shot Degradation-Blind Image Restoration
• Authors: Hamadi Chihaoui, Paolo Favaro,
• Abstract summary: Invert2Restore is a zero-shot, training-free method that operates in both fully blind and partially blind settings.<n>It generalizes well across various types of image degradation.<n>We experimentally validate Invert2Restore across several image restoration tasks.
• Score: 19.263005158979567
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Two of the main challenges of image restoration in real-world scenarios are the accurate characterization of an image prior and the precise modeling of the image degradation operator. Pre-trained diffusion models have been very successfully used as image priors in zero-shot image restoration methods. However, how to best handle the degradation operator is still an open problem. In real-world data, methods that rely on specific parametric assumptions about the degradation model often face limitations in their applicability. To address this, we introduce Invert2Restore, a zero-shot, training-free method that operates in both fully blind and partially blind settings -- requiring no prior knowledge of the degradation model or only partial knowledge of its parametric form without known parameters. Despite this, Invert2Restore achieves high-fidelity results and generalizes well across various types of image degradation. It leverages a pre-trained diffusion model as a deterministic mapping between normal samples and undistorted image samples. The key insight is that the input noise mapped by a diffusion model to a degraded image lies in a low-probability density region of the standard normal distribution. Thus, we can restore the degraded image by carefully guiding its input noise toward a higher-density region. We experimentally validate Invert2Restore across several image restoration tasks, demonstrating that it achieves state-of-the-art performance in scenarios where the degradation operator is either unknown or partially known.

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