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

• URL: http://arxiv.org/abs/2608.20141v1
• Date: Thu, 20 Aug 2026 15:04:17 GMT
• Title: DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration
• Abstract summary: We propose a novel Dual-Prior Collaborative Network (DPC-Net) to restore degraded images.<n>DPC-Net exploits degradation-semantic coupled priors and low-level visual priors.<n>Experiments on multiple restoration benchmarks demonstrate that DPC-Net achieves superior performance against state-of-the-art AiOIR methods.
• Score: 29.05613521811981
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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