ExpandDiff: Dynamic Range Expanding Diffusion for Single-Image HDR Reconstruction

arXiv:2609.39624 · cs.CV, eess.IV · Submitted 2026-09-30 · Read on arXiv

cs.CV, eess.IV

Submitted: 2026-09-30

Updated: 2026-09-30

Project page: https://memreandiran.github.io/expanddiff

Terminology

• Benchmark — 10× in this paper · explained in 2 episodes
• Dynamic Clipping Synthesis — 6× in this paper
• Alignment — 5× in this paper · explained in 1 episode
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Latent Space — 3× in this paper · explained in 7 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes

Sources

• X2HDR: HDR Image Generation in a Perceptually Uniform Space
• ExpoCM: Exposure-Aware One-Step Generative Single-Image HDR Reconstruction
• AIM 2025 challenge on Inverse Tone Mapping Report: Methods and Results
• HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content

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