Deep Inverse Tone Mapping Optimized for High Dynamic Range Display

Katsuhiko Hirao; Zhengxue Cheng; Masaru Takeuchi; Jiro Katto

doi:10.1109/ICTC.2018.8539648

Deep Inverse Tone Mapping Optimized for High Dynamic Range Display

Katsuhiko Hirao, Zhengxue Cheng, Masaru Takeuchi, Jiro Katto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

The popularity of high dynamic range (HDR) makes the inverse tone mapping become an important technique for HDR display. In this paper, we propose a convolutional neural network (CNN) based inverse tone mapping method to generate a high-quality HDR image from one single standard dynamic range (SDR) image. First, we present a CNN design with a three-channel input, which considers both luminance and chrominance. Second, we propose to use overlapped inputs to remove the boundary artifacts, caused by zero padding in CNN. Experimental results demonstrate the high quality of our generated HDR images compared to the ground truth and conventional inverse tone mapping methods.

Original language	English
Title of host publication	9th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	ICT Convergence Powered by Smart Intelligence, ICTC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	472-474
Number of pages	3
ISBN (Electronic)	9781538650400
DOIs	https://doi.org/10.1109/ICTC.2018.8539648
Publication status	Published - 2018 Nov 16
Event	9th International Conference on Information and Communication Technology Convergence, ICTC 2018 - Jeju Island, Korea, Republic of Duration: 2018 Oct 17 → 2018 Oct 19

Other

Other	9th International Conference on Information and Communication Technology Convergence, ICTC 2018
Country/Territory	Korea, Republic of
City	Jeju Island
Period	18/10/17 → 18/10/19

Keywords

convolutional neural networks
high dynamic range imaging
inverse tone mapping

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management
Artificial Intelligence

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10.1109/ICTC.2018.8539648

Cite this

Hirao, K., Cheng, Z., Takeuchi, M., & Katto, J. (2018). Deep Inverse Tone Mapping Optimized for High Dynamic Range Display. In 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018 (pp. 472-474). [8539648] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTC.2018.8539648

Deep Inverse Tone Mapping Optimized for High Dynamic Range Display. / Hirao, Katsuhiko; Cheng, Zhengxue; Takeuchi, Masaru et al.

9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 472-474 8539648.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hirao, K, Cheng, Z, Takeuchi, M & Katto, J 2018, Deep Inverse Tone Mapping Optimized for High Dynamic Range Display. in 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018., 8539648, Institute of Electrical and Electronics Engineers Inc., pp. 472-474, 9th International Conference on Information and Communication Technology Convergence, ICTC 2018, Jeju Island, Korea, Republic of, 18/10/17. https://doi.org/10.1109/ICTC.2018.8539648

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title = "Deep Inverse Tone Mapping Optimized for High Dynamic Range Display",

abstract = "The popularity of high dynamic range (HDR) makes the inverse tone mapping become an important technique for HDR display. In this paper, we propose a convolutional neural network (CNN) based inverse tone mapping method to generate a high-quality HDR image from one single standard dynamic range (SDR) image. First, we present a CNN design with a three-channel input, which considers both luminance and chrominance. Second, we propose to use overlapped inputs to remove the boundary artifacts, caused by zero padding in CNN. Experimental results demonstrate the high quality of our generated HDR images compared to the ground truth and conventional inverse tone mapping methods.",

keywords = "convolutional neural networks, high dynamic range imaging, inverse tone mapping",

author = "Katsuhiko Hirao and Zhengxue Cheng and Masaru Takeuchi and Jiro Katto",

year = "2018",

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doi = "10.1109/ICTC.2018.8539648",

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N2 - The popularity of high dynamic range (HDR) makes the inverse tone mapping become an important technique for HDR display. In this paper, we propose a convolutional neural network (CNN) based inverse tone mapping method to generate a high-quality HDR image from one single standard dynamic range (SDR) image. First, we present a CNN design with a three-channel input, which considers both luminance and chrominance. Second, we propose to use overlapped inputs to remove the boundary artifacts, caused by zero padding in CNN. Experimental results demonstrate the high quality of our generated HDR images compared to the ground truth and conventional inverse tone mapping methods.

AB - The popularity of high dynamic range (HDR) makes the inverse tone mapping become an important technique for HDR display. In this paper, we propose a convolutional neural network (CNN) based inverse tone mapping method to generate a high-quality HDR image from one single standard dynamic range (SDR) image. First, we present a CNN design with a three-channel input, which considers both luminance and chrominance. Second, we propose to use overlapped inputs to remove the boundary artifacts, caused by zero padding in CNN. Experimental results demonstrate the high quality of our generated HDR images compared to the ground truth and conventional inverse tone mapping methods.

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KW - inverse tone mapping

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Deep Inverse Tone Mapping Optimized for High Dynamic Range Display

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Keywords

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