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The paper introduces a retrieval-guided glyph-aware restoration framework for low-resource Manchu historical documents. Unlike traditional pixel-level reconstruction methods, it incorporates glyph-level structural knowledge by retrieving relevant glyph exemplars to guide the restoration process. Experiments show that this approach improves both image quality and glyph fidelity compared to existing methods.
By Ting Huang, Dongdong Wang, Mingqiu Liang, Siyang Lu
arXiv:2607. 07051v1 Announce Type: cross Abstract: Conversational image editing requires preserving not only visible content, but also content that temporarily disappears across turns.
By Soomin Han, Jihyung Ahn, Bumsoo Kim, Buru Chang
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...
The paper introduces ImIR, a method that tunes a large pretrained image‑editing model for all‑in‑one image restoration by replacing text prompts with continuous image‑derived instructions. The approach uses a lightweight token mapper to shift the degraded image’s vision‑language embedding toward that of a clean image, enabling a single adapter to handle six restoration tasks in about three hours on one GPU. ImIR outperforms text conditioning in matched comparisons and supports task‑agnostic restoration without requiring a degradation label.
By S\"uleyman Aslan, G\"orkay Aydemir, M{\i}sra Yavuz, Yunus Bilge Kurt, Nasrin Rahimi, Ahmet Rasim Emirda\u{g}{\i}, Burak Can Biner, M. Ak{\i}n Y{\i}lmaz
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