LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results

TL;DR

This paper reviews the LoViF 2026 Challenge, which benchmarks diverse real-world image restoration methods, highlighting progress and establishing a standard for future research in the field.

Contribution

It provides a comprehensive analysis of submitted methods and results, advancing the understanding of unified real-world image restoration techniques.

Findings

Significant progress in real-world all-in-one image restoration methods.

Effective approaches identified for handling diverse degradation types.

Benchmark established for evaluating robustness and generalization in image restoration.

Abstract

This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provided a unified benchmark to evaluate the robustness and generalization ability of restoration models across multiple degradation categories within a common framework. The competition attracted 124 registered participants and received 9 valid final submissions with corresponding fact sheets, significantly contributing to the progress of real-world all-in-one image restoration. This report provides a detailed analysis of the submitted methods and corresponding results, emphasizing recent progress in unified real-world image restoration. The analysis highlights effective approaches and establishes a benchmark for future research in real-world low-level vision.