PrefLUT: Reusable and Refinable Personalized Color Editing from Pairwise Preferences

arXiv:2609.34133 · cs.CV, cs.AI · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-28

Updated: 2026-09-28

Project page: https://tememuruts.github.io/RefRetouch-Project-Page/19

Terminology

• Ablation — 20× in this paper · explained in 1 episode
• Transformer — 17× in this paper · explained in 2 episodes
• Adaptation — 13× in this paper · explained in 1 episode
• Fidelity — 11× in this paper · explained in 2 episodes
• Adam — 10× in this paper · explained in 2 episodes
• Personalization — 9× in this paper · explained in 2 episodes
• Aggregation — 7× in this paper · explained in 1 episode
• Verification — 7× in this paper · explained in 2 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Comparative Quality Score — 3× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Quantization — 3× in this paper · explained in 12 episodes
• User Preference Embedding — 3× in this paper
• Ablation Studies — 2× in this paper · explained in 1 episode
• Base Fidelity Score — 2× in this paper
• Comparative Margin Ratio — 2× in this paper
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Adaptive Intervals — 1× in this paper · explained in 1 episode
• Alignment Scores — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• D PREFERENCE-CONDITIONING VERIFICATION PROTOCOL — 1× in this paper
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 15 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Personalized Photographic Style Dataset — 1× in this paper
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• FlowLUT: Efficient Image Enhancement via Differentiable LUTs and Iterative Flow Matching
• 4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement
• Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer
• Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement

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