LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration

2026-10-04

AI Summary

This work addresses the parameter redundancy and limited generalization gains of existing unified ultra-high-definition (UHD) video restoration models by proposing a recurrent mixture-of-experts architecture. Specifically, we design a degradation-aware low-rank embedding module and an iterative spatiotemporal adaptive normalization module, while introducing an input-conditioned dynamic depth prediction mechanism. These components collectively establish a lightweight recurrent learning paradigm capable of efficiently handling cross-domain restoration tasks. Requiring only approximately 0.884M trainable parameters, the proposed model achieves state-of-the-art performance on both benchmark datasets and real-world scenarios for tasks such as dehazing and deraining. Ultimately, this approach enables the unified and efficient processing of diverse UHD video restoration tasks with minimal computational overhead.

Abstract

Recently, unified high-definition image restoration has attracted considerable attention; however, existing models tend to excessively increase their depth in pursuit of improved generalization, which often yields only limited gains. Meanwhile, loop-based learning paradigms have drawn widespread attention due to their low parameter counts and strong regression capability, as exemplified by GPT-6 and looped Transformers. In this paper, we introduce the loop learning paradigm to address restoration tasks that require cross-domain learning. Specifically, we propose LoopMoEVR, a loop-based mixture-of-experts model capable of handling degraded ultra-high-definition (UHD) inputs. First, a degradation-conditioned low-rank loop embedding is designed to construct input-dependent stage conditions. Second, a spatio-temporal iterative adaptive normalization module, termed IterAda3DN, is developed to fuse local features with global loop context, thereby performing position-wise affine modulation. Finally, the expert branches further integrate the attention-updated local and global video states with the loop conditions to generate dedicated modulation parameters, while an input-conditioned depth predictor adaptively configures the number of loop iterations. With only approximately 0.884M trainable parameters, the proposed model uniformly handles UHD video dehazing, deraining, denoising, and low-light enhancement tasks, achieving state-of-the-art restoration performance on both public benchmarks and real-world scenarios.

Problem

• Video Restoration
• Ultra-High-Definition (UHD)
• Unified Restoration
• Cross-Domain Learning
• Degradation-Aware

Innovation

• Loop-based Learning
• Mixture-of-Experts
• UHD Video Restoration
• Degradation-Aware
• Adaptive Normalization