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

• URL: http://arxiv.org/abs/2605.08627v1
• Date: Sat, 09 May 2026 02:43:00 GMT
• Title: DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization
• Abstract summary: All-in-one image restoration aims to handle diverse degradations within a single model.<n>Existing methods often suffer from three key limitations.<n>DRNet achieves state-of-the-art performance across five restoration tasks with superior parameter efficiency.
• Score: 45.271664532141294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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