#image restoration
7 papers · 1 filter
What to Remove, What to Preserve: Dual-Ambiguity Rectification for All-in-One Image Restoration
Cencen Liu, Wen Yin, Dongyang Zhang +6
The paper introduces DAR-Net, a deep network that tackles the dual ambiguity problem in all‑in‑one image restoration by modeling degradation states with a simplex‑constrained arche…
CoRE-UIR: Prior-guided common and residual experts for efficient all-in-one remote sensing image restoration
Zaiyan Zhang, Qiangqiang Yuan, Jie Li +5
The paper introduces CoRE-UIR, a prior‑guided framework that separates restoration into a common dense expert and low‑rank residual experts to efficiently handle multiple degradati…
SPFM-Net: Semantic-Prior-Guided Frequency-Constrained Mamba for Invisible Watermark Attack
Chunpeng Wang, Yanan Shi, Zhiqiu Xia +3
The paper introduces SPFM-Net, a neural network that uses semantic priors, frequency constraints, and a lightweight Mamba-based state‑space module to remove invisible watermarks fr…
QuReC: All-in-One Image Restoration with Query-Specific Guidance and Local-Global Response Calibration
Shen Zhou, Jinghui Zhang, Wenbo Huang +7
QuReC is a unified model for restoring images affected by multiple, possibly mixed, degradations by using spatially adaptive, query-specific guidance and a dual local‑global featur…
Thresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement
Yanyi Wu, Xu Zhang, Junkai Chen +6
The paper introduces TCA-Net, which uses a thresholded cross-attention mechanism to more reliably fuse intensity and chromaticity information for low-light image enhancement, impro…
ClusIR: Towards Cluster-Guided All-in-One Image Restoration
Shengkai Hu, Jiaqi Ma, Xu Zhang +3
ClusIR introduces a cluster-guided framework that learns degradation semantics via clustering and uses these cues to adaptively restore images across spatial and frequency domains.