[Submitted on 21 Sep 2026]
Title:ImIR: Image-Instruction Tuning for All-in-One Image Restoration
View a PDF of the paper titled ImIR: Image-Instruction Tuning for All-in-One Image Restoration, by S\"uleyman Aslan and 7 other authors
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Abstract: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 model to restoration using a small low-rank adapter with a text prompt. We replace that prompt with an instruction derived from the degraded image itself. The image reaches the editor through two paths: its structure comes from the model's VAE, and its semantic instruction comes from a lightweight token mapper that shifts the degraded image's vision-language embedding toward the embedding a clean image would produce. Because the instruction is a continuous vector, scaling it yields a family of valid restorations for tasks whose target is not unique, such as low-light enhancement. We adapt one Qwen-Image-Edit model to six tasks with a single adapter trained in about three hours on one GPU. The image instruction outperforms text conditioning under a matched comparison, and it supports task agnostic restoration without a degradation label, which the text variant does not.
Comments: Accepted to ACCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.25267 [cs.CV]
(or arXiv:2609.25267v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.25267
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Süleyman Aslan [view email] [v1] Mon, 21 Sep 2026 18:15:08 UTC (3,558 KB)
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