A Review of All-in-One Image Restoration Techniques

Abstract

Image restoration (IR) refers to the process of eliminating image degradations—such as noise, blur, and weather effects like rain or haze—while enhancing visual quality. Traditional IR methods typically target specific types of degradation, which limits their effectiveness in real-world scenarios involving complex distortions. To address this challenge, the All-in-One Image Restoration (AiOIR) paradigm has emerged, offering a unified framework adept at resolving multiple degradation types. These innovative models integrate degradation-specific adaptive learning with cross-task knowledge sharing, significantly enhancing their flexibility and versatility. This paper systematically reviews existing AiOIR techniques and provides an in-depth analysis of their innovations in architectural evolution and feature learning paradigms. To advance this dynamic field, we present a comprehensive and profound review of AiOIR technologies. The narrative structure of this article is organized as follows: first, we elaborate on the foundational concepts of AiOIR; second, we systematically categorize and review mainstream cutting-edge designs based on key factors such as prior features and generalization capability; building upon this, we highlight the core technological advancements in the field; finally, we critically evaluate the bottlenecks and challenges encountered by current models and identify future research directions, with the aim of inspiring broader academic exploration and innovation.