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arXiv · 2109.00180

Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping

Chenyang Le·Jiebin Yan·Yuming Fang·Kede Ma
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Abstract

We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized. We first decompose an HDR image into a normalized Laplacian pyramid, and use two deep neural networks (DNNs) to estimate the Laplacian pyramid of the desired tone-mapped image from the normalized representation. We then end-to-end optimize the entire method over a database of HDR images by minimizing the normalized Laplacian pyramid distance (NLPD), a recently proposed perceptual metric. Qualitative and quantitative experiments demonstrate that our method produces images with better visual quality, and runs the fastest among existing local tone mapping algorithms.

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Chenyang Le, Jiebin Yan, Yuming Fang, Kede Ma. 2021-09-01. Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping. https://arxiv.org/abs/2109.00180

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