Virtual Consistency Model for All-in-One Image Restoration

Abstract

All-in-one Image Restoration (AIR) seeks to address diverse degradations using a unified model trained only once. Existing methods often rely on degradation-specific guidance, leading to conflicting gradients during training. In contrast, diffusion models offer a promising alternative by operating in a high-noise space where diverse degradations exhibit a homogeneous Gaussian distribution. This characteristic alleviates gradient conflicts associated with task-specific degradations. However, existing diffusion-based AIR methods often suffer from a lack of direct supervision in the image space, leading to error accumulation during the iterative denoising process and image fidelity compromisation. This highlights a fundamental dilemma for AIR: the optimal space for modeling degradations is inherently suboptimal for preserving image fidelity. To address this issue, we propose a Virtual Consistency Model for AIR (VCMAIR), which restores images in the high-noise space while employing a novel consistency function to enforce accurate supervision in the image space. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art methods across a comprehensive benchmark of diverse degradation scenarios, including both standard AIR tasks and challenging real-world image restoration tasks.