HDR image generation method based on conditional generative adversarial network

被引：0
|
作者：
Bei Y. [1 ]
Wang Q. [1 ]
Cheng Z. [1 ]
Pan X. [1 ]
Yang M. [2 ]
Ding D. [2 ]
机构：
[1] MIGU Video Co., Ltd., Shanghai
[2] Beijing Bravo Video Technologies Incorporation, Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2021年 / 48卷 / 01期
关键词：
Conditional generative adversarial network (CGAN); Convolutional neural network; Feature extraction; Gamut mapping; Inverse tone mapping;
D O I：
10.13700/j.bh.1001-5965.2020.0518
中图分类号：
学科分类号：
摘要：
Compared with low dynamic range (LDR) images, high dynamic range (HDR) images have a wider color gamut and higher brightness range, which is more in line with human visual effects. However, since most of the current image acquisition devices are LDR devices, HDR image resources are scarce. An effective way to solve this problem is to map LDR images to HDR images through inverse tone mapping. This paper proposes an inverse tone mapping algorithm based on conditional generative adversarial network (CGAN) to reconstruct HDR images. To this end, a multi-branch-based generation network and a discrimination network based on discrimination blocks are designed, and the data generation and feature extraction capabilities of CGAN are used to map a single LDR image from the BT. 709 color gamut to the corresponding BT. 2020 color area. The experimental results show that the proposed network can obtain higher objective and subjective quality compared with the existing methods. Especially for fuzzy areas in the low color gamut, the proposed method can reconstruct clearer textures and details. © 2022, Editorial Board of JBUAA. All right reserved.
引用
收藏
页码：45 / 52
页数：7
相关论文
共 20 条
  • [1] MA Z X., HDR technology and application on 4K ultra-high-definition TV, Television Technology, 43, 1, pp. 33-39, (2019)
  • [2] ENDO Y, KANAMORI Y, MITANI J., Deep reverse tone mapping, ACM Transactions on Graphics, 36, 6, pp. 177:1-177:10, (2017)
  • [3] EILERTSEN G, KRONANDER J, DENES G, Et al., HDR image reconstruction from a single exposure using deep CNNs, ACM Transactions on Graphics, 36, 6, pp. 1-15, (2017)
  • [4] XU Y C, SONG L, XIE R, Et al., Deep video inverse tone mapping, 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 142-147, (2019)
  • [5] MARNERIDES D, BASHFORD-ROGERS T, HATCHETT J, Et al., ExpandNet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content, Computer Graphics, 37, 2, pp. 37-49, (2018)
  • [6] KINOSHITA Y, KIYA H., iTM-Net: Deep inverse tone mapping using novel loss function considering tone mapping operator, IEEE Access, 7, pp. 73555-73563, (2019)
  • [7] LEE S, AN G H, KANG S J., Deep chain HDRI: Reconstructing a high dynamic range image from a single low dynamic range image, IEEE Access, 6, pp. 49913-49924, (2018)
  • [8] XU Y C, NING S Y, XIE R, Et al., GAN based multi-exposure inverse tone mapping, 2019 IEEE International Conference on Image Processing (ICIP), pp. 1-5, (2019)
  • [9] NING S Y, XU H T, SONG L, Et al., Learning an inverse tone mapping network with a generative adversarial regularizer, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1383-1387, (2018)
  • [10] LEE S, AN G H, KANG S J., Deep recursive HDRI: Inverse tone mapping using generative adversarial networks, Proceedings of the European Conference on Computer Vision (ECCV), pp. 596-611, (2018)
12