Adverse weather image restoration aims to recover clean background scenes from images degraded by various weather conditions, such as haze, rain, and snow. With the rapid development of deep learning, single-task restoration methods targeting specific weather types have achieved remarkable progress and attracted increasing attention in recent years. More recently, to address the limited generalization of task-specific models, All-in-One (AiO) methods have emerged to handle multiple degradations within a unified framework. However, existing surveys mostly focus on individual degradation types or specific restoration paradigms, and unified reviews of deep learning-based adverse weather restoration are still limited. In this paper, we present a comprehensive survey that jointly organizes single-task and AiO restoration models from the perspectives of network architectures and learning paradigms. We further review widely used datasets, loss functions, and evaluation metrics across different restoration tasks. In addition, we summarize benchmark results of representative methods on public datasets to analyze their performance and generalization ability. Finally, we discuss key challenges and promising research directions to support future developments in this rapidly evolving field.
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.
Jiawei Wu, Luwei Tu, Zhe Wang et al.· IEEE Transactions on Image P...· 0 citations
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