| 基于条件生成对抗网络的HDR图像生成方法 |
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| 引用本文： | 贝悦,王琦,程志鹏,潘兴浩,杨默涵,丁丹丹. 基于条件生成对抗网络的HDR图像生成方法[J]. 北京航空航天大学学报, 2022, 48(1): 45-52. DOI: 10.13700/j.bh.1001-5965.2020.0518 |
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| 作者姓名： | 贝悦 王琦 程志鹏 潘兴浩 杨默涵 丁丹丹 |
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| 作者单位： | 1.咪咕视讯科技有限公司, 上海 201201 |
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| 基金项目： | 浙江省自然科学基金(LY20F010013)~~； |
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| 摘 要： | 高动态范围（HDR）图像相比低动态范围（LDR）图像有更宽的色域和更高的亮度范围，更符合人眼视觉效果，但由于目前的图像采集设备大都是LDR设备，导致HDR图像资源匮乏，解决该问题的一种有效途径是通过逆色调映射将LDR图像映射为HDR图像。提出了一种基于条件生成对抗网络（CGAN）的逆色调映射算法，以重建HDR图像。为此，设计了基于多分支的生成对抗网络与基于鉴别块的鉴别网络，并利用CGAN的数据生成能力和特征提取能力，将单张LDR图像从BT.709色域映射到对应的BT.2020色域。实验结果表明:与现有方法相比，所提出的网络能够获得更高的客观与主观质量，特别是针对低色域中的模糊区域，所提方法能够重建出更清晰的纹理与细节。
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| 关 键 词： | 条件生成对抗网络(CGAN) 卷积神经网络 逆色调映射 色域转换 特征提取 |
| 收稿时间： | 2020-09-14 |
HDR image generation method based on conditional generative adversarial network |
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| BEI Yue,WANG Qi,CHENG Zhipeng,PAN Xinghao,YANG Mohan,DING Dandan. HDR image generation method based on conditional generative adversarial network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(1): 45-52. DOI: 10.13700/j.bh.1001-5965.2020.0518 |
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| Authors: | BEI Yue WANG Qi CHENG Zhipeng PAN Xinghao YANG Mohan DING Dandan |
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| Affiliation: | 1.MIGU Video Co., Ltd., Shanghai 201201, China2.Beijing Bravo Video Technologies Incorporation, Beijing 100036, China |
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| Abstract: | 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. |
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