QuReC: All-in-One Image Restoration with Query-Specific Guidance and Local-Global Response Calibration

• URL: http://arxiv.org/abs/2607.15097v2
• Date: Sat, 18 Jul 2026 17:48:09 GMT
• Title: QuReC: All-in-One Image Restoration with Query-Specific Guidance and Local-Global Response Calibration
• Abstract summary: All-in-one image restoration aims to recover clean images degraded by multiple corruption types using a single unified model.<n>QuReC consists of a Degradation Reconstruction Module (DQRM) and a Local-Global Response Module (LGRCM)<n>DQRM matches each spatial query against a degradation prototype space to reconstruct a query-specific degradation representation.<n>LGRCM performs local-global dual-branch aggregation and calibrates the aggregated responses with learnable priors.
• Score: 20.33280733198936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: All-in-one image restoration aims to recover clean images degraded by multiple corruption types using a single unified model. Existing methods typically rely on image-level prompts or shared guidance to handle diverse degradations. However, such a paradigm becomes inadequate when degradations are spatially heterogeneous or even coexist in mixed forms within a single image. Yet spatially adaptive guidance alone is not sufficient, since accurate restoration also requires each spatial query to reliably aggregate complementary information from local neighborhoods and global contexts. To this end, we propose QuReC, a unified framework for all-in-one image restoration. QuReC consists of a Degradation-Guided Query Reconstruction Module (DQRM) and a Local-Global Response Calibration Module (LGRCM). Specifically, DQRM matches each spatial query against a degradation prototype space to reconstruct a query-specific degradation-aware representation, thereby providing fine-grained spatially adaptive restoration guidance. To further stabilize this query-wise matching process, we introduce a weakly supervised prototype matching learning strategy to improve optimization stability and degradation semantic consistency. Meanwhile, LGRCM performs local-global dual-branch aggregation and calibrates the aggregated responses with learnable priors, improving the reliability of feature aggregation and the coordination between local detail modeling and global context modeling. Extensive experiments demonstrate that QuReC achieves superior performance on multiple all-in-one image restoration benchmarks. The code is released at https://github.com/zhoushen1/QuReC.

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