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

2026-04-21

AI Summary

Real-world images are often simultaneously degraded by multiple factors such as blur, low light, haze, and rain or snow, yet existing methods lack a unified evaluation benchmark and general-purpose restoration models. To address this gap, this work introduces the LoViF 2026 Challenge, which establishes the first comprehensive, all-in-one benchmark for image restoration under realistic, multi-degradation scenarios. The benchmark integrates diverse degradation types within a unified dataset and employs an end-to-end evaluation protocol. Attracting 124 participating teams and yielding nine valid submissions, the challenge not only sets a definitive standard for objective assessment but also systematically identifies effective strategies for general-purpose restoration, significantly advancing research on model robustness and generalization in complex real-world conditions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• real-world image restoration
• all-in-one restoration
• image degradation
• low-level vision
• unified benchmark

Innovation

• all-in-one image restoration
• real-world degradation
• unified benchmark
• low-level vision
• image restoration challenge