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

2026-09-30 12:35:04
Mehmet Emre and{\i}ran, Zhuoqian Yang, Liying Lu, Mathieu Salzmann, Sabine S\"usstrunk
arXiv_CV
     
Abstract

Single-image HDR reconstruction requires inferring missing detail while preserving the visible content of an LDR image. Differences in sensor dynamic range and exposure cause LDR images to lose varying amounts of information in shadows and highlights. We present ExpandDiff, a conditional diffusion pipeline that jointly reconstructs clipped shadows and highlights. To account for this variation, we introduce Dynamic Clipping Synthesis (DCS), which randomly samples shadow and highlight clipping percentiles when constructing training inputs from HDR targets. A pixel-space diffusion model guided by spatially-adaptive normalization then predicts perceptually encoded HDR through a bounded output head, reconstructing both clipping directions in one sampling trajectory. On the SI-HDR benchmark, ExpandDiff variants improve HDR reconstruction accuracy by 3.43 dB in PU21-PSNR over the strongest evaluated competing method, and by 7.34 dB under two-sided clipping. The code and supplementary material are available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2609.39624

PDF

https://arxiv.org/pdf/2609.39624.pdf

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