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.
By Jie Shao, Shengkai Hu, Xu Zhang, Beihang Song, Yongcheng Jing, Xu Wu, Jun Wan
The paper introduces Restoring without Forgetting (RwF), a continual learning framework for image restoration that handles multiple degradations sequentially without accessing prior data. RwF trains a lightweight adapter for each new degradation, uses an unsupervised routing mechanism to select the correct restoration path, and achieves significant PSNR gains over fine‑tuning on five benchmark degradation domains. The method also demonstrates strong transfer performance on eleven real‑degradation datasets with high routing accuracy.
By Alif Ashrafee, Bartosz Krawczyk
arXiv:2606. 28112v1 Announce Type: cross Abstract: Degradation-aware prompts, conditions, and latent priors are increasingly used in image restoration, yet they are usually judged by a single endpoint: whether the restored image obtains higher PSNR.
By Xinrui Wu, Lichen Huang
arXiv:2605.22208v3 Announce Type: replace
Abstract: Multimodal Large Language Model (MLLM)-driven image restoration agent demonstrates effectiveness in degradation coupling scenarios by flexibly sele...
By Kailin Zhuang, Jiawei Wu, Zhi Jin
arXiv:2609.38591v1 Announce Type: new
Abstract: Adapting image restoration models to a stream of new tasks without revisiting past data remains challenging due to catastrophic forgetting. In this wor...
By Xin Feng, Jin Zhao, Yizhen Zhang, Wenjie Pei, Fanglin Chen, Guangming Lu
Remote sensing images acquired by unmanned aerial vehicles (UAVs) and satellites are often degraded by adverse weather, illumination variation, and imaging artifacts, which may co-occur and jointly induce global distribution shifts and local structural corruption. Although All-in-One image restoration offers an appealing unified alternative to task-specific pipelines, existing methods still suffer from weak or implicit degradation cues and parameter redundancy caused by full-rank multi-expert designs with overlapping restoration behaviors.
arXiv:2608. 09482v1 Announce Type: cross Abstract: All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption using a single model.
By Chunxiao Liu, Wei Liu, Anbin Xiong, Erli Meng
The paper introduces DLC, a metric-guided dynamic loss controller that adjusts the weighting of existing training losses during multi-objective image restoration. DLC evaluates the model on a fixed feedback subset and uses quality metrics to update the loss-weight vector via an LLM-based controller, without altering the restoration architecture or adding new loss terms. Experiments on low-light enhancement, deraining, and super-resolution demonstrate that DLC guides training toward balanced fidelity and perceptual quality across diverse domains.
By Jaewan Ko, Janghoon Choi
arXiv:2609.13791v1 Announce Type: new
Abstract: Recognition pipelines typically adopt a restore-then-recognize workflow, yet decades of experience show that generating visually pleasing images seldom...
By Lanqing Guo, Xijun Wang, Minchul Kim, Yu Yuan, Wes Robbins, Xingguang Zhang, Nicholas Chimitt, Stanley H. Chan, Zhangyang Wang, Xiaoming Liu
Deep Repurposing (DR) is a post‑hoc framework that adapts deep neural networks when parts of their output space become obsolete after deployment. DR estimates the latent geometry of obsolete and retained regions, removes components that support obsolete outputs, and reallocates retained-compatible evidence through an analytic repair map without gradient updates. The method yields repaired predictions and representations that eliminate obsolete outputs while preserving or improving retained accuracy, and it adapts up to 60× faster than competing unlearning methods.
By Daniel Bethell, Charmaine Barker, Simos Gerasimou
arXiv:2609.31170v1 Announce Type: new
Abstract: Task-driven image restoration aims to improve both image quality and downstream task performance. However, existing methods predominantly focus on sing...
By Yanjie Tu, Qingsen Yan, Axi Niu, Wenxuan Cai, Tao Hu, Wei Dong, Haokui Zhang
EvoKnow is a replay‑free framework for continual AI‑generated image detection that treats forensic knowledge as evolving. It keeps a shared forensic basis from base domains, adds isolated residual experts for new generators, and uses an Analytical Incremental Router (AIR) updated in closed form from current‑stage data and accumulated statistics. Experiments show strong cross‑generator generalization, few‑shot expansion, and low forgetting, achieving 96.70% accuracy on non‑base generators and 96.32% mean stage‑wise accuracy under a strict replay‑free protocol.
By Zhiheng Peng, Wenwei Jin, Yangshi Ge, Siyu Xia, Jiawei Li, Xu Tang