Deep image restoration in adverse weather: A survey.

Abstract

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.