A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends

TL;DR

This survey comprehensively reviews all-in-one image restoration methods that handle multiple degradation types within a unified framework, highlighting taxonomy, evaluation practices, and future research directions.

Contribution

It provides the first systematic overview and taxonomy of AiOIR methods, consolidates datasets and evaluation protocols, and compares leading models to guide future research.

Findings

Categorized existing AiOIR approaches by architecture and learning paradigms.

Identified key challenges and research directions in AiOIR.

Summarized datasets, evaluation protocols, and open-source models.

Abstract

Image restoration (IR) seeks to recover high-quality images from degraded observations caused by a wide range of factors, including noise, blur, compression, and adverse weather. While traditional IR methods have made notable progress by targeting individual degradation types, their specialization often comes at the cost of generalization, leaving them ill-equipped to handle the multifaceted distortions encountered in real-world applications. In response to this challenge, the all-in-one image restoration (AiOIR) paradigm has recently emerged, offering a unified framework that adeptly addresses multiple degradation types. These innovative models enhance the convenience and versatility by adaptively learning degradation-specific features while simultaneously leveraging shared knowledge across diverse corruptions. In this survey, we provide the first in-depth and systematic overview of AiOIR, delivering a structured taxonomy that categorizes existing methods by architectural designs, learning paradigms, and their core innovations. We systematically categorize current approaches and assess the challenges these models encounter, outlining research directions to propel this rapidly evolving field. To facilitate the evaluation of existing methods, we also consolidate widely-used datasets, evaluation protocols, and implementation practices, and compare and summarize the most advanced open-source models. As the first comprehensive review dedicated to AiOIR, this paper aims to map the conceptual landscape, synthesize prevailing techniques, and ignite further exploration toward more intelligent, unified, and adaptable visual restoration systems. A curated code repository is available at https://github.com/Harbinzzy/All-in-One-Image-Restoration-Survey.