DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration

arXiv:2608.20141v1 Announce Type: new Abstract: All-in-One Image Restoration (AiOIR) aims to handle diverse degradations within a unified model. However, existing methods often overlook image semantics in degradation modeling and lack low-level visual priors during reconstruction, leading to structural distortions and semantic inconsistencies. To address these issues, we propose a novel Dual-Prior Collaborative Network (DPC-Net), which achieves high-quality restoration by jointly exploiting degradation-semantic coupled priors and low-level visual priors. Specifically, degraded images are fed into a Degradation-Aware Network (DAN) to extract degradation-semantic coupled features. To this end, a Vision-Language Model (VLM) supervises DAN by constraining its features distribution, introducing image semantics into the encoding of degradation patterns. A Degradation-Semantic Modulation Module (DSMM) further translates this guidance into degradation-semantic coupling and propagates coupled representations to the decoder. During decoding, knowledge bases provide low-level visual priors, and the Dual-Prior Collaborative Reconstruction Module (DPCR) integrates dual-prior information to guide degradation removal while preserving structure and semantics, producing high-fidelity restored images. Extensive experiments on multiple restoration benchmarks demonstrate that DPC-Net achieves superior performance against state-of-the-art AiOIR methods.

Related

• DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization
• Degradation-Aware Adaptive Context Gating for Unified Image Restoration
• What to Remove, What to Preserve: Dual-Ambiguity Rectification for All-in-One Image Restoration
• RetinexDualV2: Physically-Grounded Dual Retinex for Generalized UHD Image Restoration
• ClusIR: Towards Cluster-Guided All-in-One Image Restoration

Source: arXiv cs.CV | 2026-08-21