Unsupervised single-image high dynamic range rendering via multi-exposure priors

Abstract

Reconstructing high dynamic range (HDR) images from a single low dynamic range (LDR) input requires recovering missing information in highlight-clipped and shadow-distorted regions. Existing methods generally rely on sufficient ground-truth HDR images as supervision signals or multi-exposure LDR sequences to improve quality, limiting their flexibility. To address this, we propose USME-HDR, a framework for single-image HDR reconstruction based on multi-exposure priors, where the HDR reconstruction stage is learned without ground-truth HDR supervision. Specifically, an exposure-adjustment network (EAN) is trained in a supervised manner to map a single LDR image to over/under-exposure pairs. Inspired by the Retinex theory, we further decompose the input into a light map and a light feature, which are fed into the EAN as auxiliary inputs for luminance-aware exposure generation. An exposure time ratio guidance mechanism is further introduced to improve luminance fidelity. Finally, the HDR image is synthesized by fusing the original LDR image with generated multi-exposure images, refined through self-supervised optimization. Experiments demonstrate that during the test phase, USME-HDR reconstructs visually compelling HDR images from only a single LDR input, without requiring real low- or high-exposure images.

Keywords

High dynamic range (HDR) / HDR reconstruction / Single-image HDR / Unsupervised learning / Multi-exposure prior

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The Authors. Published by Zhejiang University Press Co., Ltd.