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Image Tone Mapping by Employing Anisotropic Total Variation and Two-Directional Gradient Prior

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Abstract

Tone mapping reproduces the true radiance map of a scene from a high dynamic range (HDR) image while preserving sharp edges. Popular approaches extract base and texture layers from an HDR image, adjust and combine them to obtain the final results. Due to the drawbacks of the priors used in layer decomposition, these methods suffer from over-enhancement and halo artifacts. In this paper, to suppress halo artifacts while preserving sharp edges, we propose a new layer decomposition model for tone mapping. We apply L1 regularized anisotropic total variation to model the piecewise part, and impose an L0 regularized two-directional gradient prior on the detail structures to achieve efficient structural layer decomposition. Experiments show that our method outperforms state-of-the-art methods on image decomposition and tone mapping.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank Dr. Xu from Nanjing university of science and technology for providing advice about this work, and thank the support by Natural Science Foundation of Huaian (HABZ202116) and Natural Science Research Project of Higher Education Institutions of Jiangsu Province (grant number 18KJB416002). We also thank Dr. Farbman, Dr. Shan, and Dr. Liang et al. for sharing the corresponding software online.

Funding

Natural Science Foundation of Huaian (HABZ202116) Huasong Chen.

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Authors and Affiliations

  1. Department of the Applied Physics, Huaiyin Institute of Technology, Huai-an, 223400, China

    Qi Zhang, Huasong Chen, Nina Hua, Qiansheng Feng & Yuanyuan Fan

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  1. Qi Zhang
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  2. Huasong Chen
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  3. Nina Hua
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Correspondence to Huasong Chen.

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Zhang, Q., Chen, H., Hua, N. et al. Image Tone Mapping by Employing Anisotropic Total Variation and Two-Directional Gradient Prior. Circuits Syst Signal Process 41, 5026–5048 (2022). https://doi.org/10.1007/s00034-022-02017-3

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