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.
Pretrained partial differential equation (PDE) foundation models can generalize across different equations, but adapting them to unseen PDE systems typically requires dense solution data, which is often expensive or unavailable. To address this limitation, we propose an unsupervised PDE-based finetuning framework that eliminates the need for ground-truth solutions. We first pretrain a neighborhood attention Transformer on diverse time-dependent PDEs spanning varying spatial scales, yielding transferable representations across heterogeneous equations. In the adaptation stage, we construct a physics-based objective using the PDE residual and boundary conditions, and finetune the model on unseen equations via low-rank adaptation (LoRA). To address the uneven learning across physical quantities in standard LoRA, we introduce NSLoRA, a Newton-Schulz orthogonalized variant that rebalances adaptation. Our method achieves performance comparable to supervised LoRA finetuning without requiring any ground-truth solutions, while consistently outperforming competitive neural operator baselines and recent PDE foundation models across heterogeneous PDE benchmarks spanning multiple spatial dimensions.
Ziye Song, Zhao Wei, Xin Yu et al.· 0 citations
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