ImIR: Image-Instruction Tuning for All-in-One Image Restoration

arXiv:2609.25267 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Terminology

• Conference on Computer Vision and Pattern Recognition — 12× in this paper
• Instruction Tuning — 10× in this paper · explained in 2 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Color Attenuation Prior — 3× in this paper
• Fidelity — 3× in this paper · explained in 2 episodes
• Inverse Problem — 3× in this paper · explained in 2 episodes
• Prompt Engineering — 3× in this paper · explained in 4 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Step1X-Edit: A Practical Framework for General Image Editing
• ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration
• LongCat-Image Technical Report
• Qwen-Image Technical Report
• RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models
• Edit2Restore:Few-Shot Image Restoration via Parameter-Efficient Adaptation of Pre-trained Editing Models

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