DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization

2026-05-08

AI Summary

Existing unified image restoration models suffer from high computational costs, optimization challenges due to task heterogeneity, and insufficient frequency-aware modeling. This work proposes DRNet, which introduces a novel dynamic reparameterization mechanism during initialization. By integrating a task-specific modulator-guided Dynamic Reparameterized MLP (DRMLP) with a Continuous Wavelet Transform Encoder (CWTE), DRNet enables efficient unified modeling that eliminates per-input computational redundancy, effectively fuses task-specific and general-purpose representations, and explicitly captures frequency-domain information. The method achieves state-of-the-art performance across five image restoration tasks, combining the strengths of both blind foundational models and user-guided expert models while demonstrating significantly higher parameter efficiency than current approaches.

Abstract

All-in-one image restoration aims to handle diverse degradations within a single model. However, existing methods often suffer from three key limitations: 1) per-input computational overhead from dynamic degradation estimation; 2) optimization challenges due to task heterogeneity; and 3) inefficient, frequency-agnostic encoder designs. To overcome these, we introduce the Dynamic Reparameterization Network (DRNet), a novel framework operating on an initialization-stage reconfiguration paradigm that fundamentally eliminates per-input overhead. At its core, a Dynamic Reparameterization MLP (DRMLP) guided by a Task-Specific Modulator (TSM), which effectively mitigates task heterogeneity by orchestrating both specific restoration goals and a versatile general-purpose mode within a unified architecture. Furthermore, we incorporate a Continuous Wavelet Transform Encoder (CWTE) that explicitly leverages frequency characteristics via wavelet decomposition for a lightweight yet powerful design. Extensive experiments demonstrate that DRNet achieves state-of-the-art performance across five restoration tasks with superior parameter efficiency. Crucially, it showcases unique flexibility, excelling as both a highly competitive foundation model for blind restoration and a top-performing user-guided specialist.

Problem

• image restoration
• task heterogeneity
• computational overhead
• frequency-agnostic encoder
• degradation estimation

Innovation

• Dynamic Reparameterization
• Task-Specific Modulator
• Continuous Wavelet Transform Encoder
• All-in-One Image Restoration
• Initialization-Stage Reconfiguration