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

2026-07-16 · 15:10 UTC ·Shen Zhou, Jinghui Zhang, Wenbo Huang... ·1 min read

Brief

QuReC targets all-in-one image restoration under spatially heterogeneous and mixed degradations by combining per-query degradation-aware guidance with robust feature aggregation. DQRM reconstructs query-specific representations via matching to a learned degradation prototype space, stabilized with weakly supervised prototype matching, while LGRCM fuses local and global responses and calibrates them with learnable priors. The model outperforms prior unified restoration methods on multiple benchmarks; code and pretrained resources are publicly released.

Why it matters

QuReC (Zhou et al., accepted at ACM MM 2026; arXiv:2607.15097v1, published 2026-07-16) introduces two modules: a Degradation-Guided Query Reconstruction Module (DQRM) that matches each spatial query to a degradation prototype space to produce query-specific degradation-aware representations, and a Local-Global Response Calibration Module (LGRCM) for dual-branch aggregation calibrated by learnable priors.

Key details

The authors add a weakly supervised prototype matching learning strategy to stabilize query-wise matching and improve degradation semantic consistency; experiments report that QuReC achieves superior performance on multiple all-in-one image restoration benchmarks and the code is released at https://github.com/zhoushen1/QuReC.

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.

Comment: Accepted by ACM MM 2026