Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping

• URL: http://arxiv.org/abs/2109.00180v2
• Date: Thu, 2 Sep 2021 04:48:04 GMT
• Title: Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping
• Authors: Chenyang Le and Jiebin Yan and Yuming Fang and Kede Ma
• Abstract summary: 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.
• Score: 44.00069411131762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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