arXiv Computer Vision
TaskIR: Task-Driven Image Restoration via Degradation Adaptation and Task Feedback
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arXiv Computer Vision
Sep 21
SkillIR: Evolving Scene-Aware Skills for Agentic Image Restoration
SkillIR is a skill-guided framework for agentic image restoration that represents restoration experience as degradation-centered action evidence rather than full tool-use trajectories. It consolidates context-dependent action outcomes into scene-aware restoration skills, guiding one bounded action at a time within a verified residual-state loop. Experiments on synthetic and real-world multi-degradation datasets show that SkillIR improves restoration quality and enables more reliable and effective tool use.
arXiv Computer Vision
Aug 28
Loop-Mamba: A Loop Mamba with Degradation-Aware and Shared Memory for Old Photo Restoration
Loop‑Mamba is a lightweight, loop‑based state‑space framework designed for restoring old photographs that suffer from multiple degradations such as scratches, cracks, fading, blur, noise, and missing regions. It models restoration as progressive state evolution, using a Semantic‑Guided Degradation Estimator to predict local degradation maps and global scores, and a Shared Structural Memory Mamba to maintain a persistent restoration state across iterations. The method employs first‑order state recursion and a multi‑directional scanning strategy to reduce gradient dilution and computational overhead, and introduces the Old Photo Damage Recovery Score (ODRS) to evaluate both degradation recovery and structural reconstruction, achieving superior performance on the SynOld benchmark.
arXiv Computer Vision
Aug 21
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.
arXiv Computer Vision
Sep 1
UniDiffFusion: A Unified Diffusion Framework for Multi-Task and Degradation-Robust Image Fusion
arXiv:2608.21786v2 Announce Type: replace Abstract: General image fusion aims to integrate complementary information from multiple source images, but existing methods often rely on task-specific mode...
arXiv Computer Vision
Sep 16
GraLoD: Graphics-Inspired Continuous Level-of-Detail Learning for Image Restoration
arXiv:2609.16578v1 Announce Type: new Abstract: The spatial support required for image restoration varies across degradation types, image regions, and reconstruction stages. However, most existing me...
Hugging Face Trending Papers
Sep 21
ImIR: Image-Instruction Tuning for All-in-One Image Restoration
Degradations vary widely across images, so a practical restoration system has to handle many degradation types with one model. A recent and effective recipe adapts a large pretrained image-editing mod...