Parallel Imaging: A New Theoretical Framework for Image Generation
WANG Kunfeng 1,2, LU Yue1,3, WANG Yutong1,3, XIONG Ziwei3, WANG Fei-Yue1,4
1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 2.Parallel Vision Innovation Technology Center, Qingdao Academy of Intelligent Industries, Qingdao 266000 3.School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049 4.Research Center of Military Computational Experiments and Parallel Systems, National University of Defense Technology, Changsha 410073
Abstract：To build computer vision systems with good generalization ability, large-scale, diversified, and annotated image data are required for learning and evaluating the in-hand computer vision models. Since it is difficult to obtain satisfying image data from real scenes, a new theoretical framework for image generation is proposed, which is called parallel imaging. The core component of parallel imaging is various software-defined artificial imaging systems. Artificial imaging systems receive small-scale image data collected from real scenes, and then generate large amounts of artificial image data. In this paper, the realization methods of parallel imaging are summarized, including graphics rendering, image style transfer, generative models, etc. Furthermore, the characteristics of artificial images and actual images are analyzed and the domain adaptation strategies are discussed.
王坤峰，鲁越，王雨桐，熊子威，王飞跃. 平行图像:图像生成的一个新型理论框架*[J]. 模式识别与人工智能, 2017, 30(7): 577-587.
WANG Kunfeng, LU Yue, WANG Yutong, XIONG Ziwei, WANG Fei-Yue. Parallel Imaging: A New Theoretical Framework for Image Generation. , 2017, 30(7): 577-587.
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