深度学习下的计算成像:现状、挑战与未来 下载： 10190次特邀综述
Deep Learning Based Computational Imaging: Status, Challenges, and Future
In recent years, optical imaging techniques have entered into the era of computational optical imaging from the traditional intensity and color imaging. Computational optical imaging, which is based on geometric optics, wave optics, and other theoretical foundations, establishes an accurate forward mathematical model for the whole image formation process of the scene imaged through the optical system and then sampled by the digital detector. Then, the high-quality reconstruction of the image and other high dimensional information, such as phase, spectrum, polarization, light field, coherence, refractive index, and three-dimension profile, which cannot be directly accessed using traditional methods, can be obtained through computational reconstruction method. However, the actual imaging performance of the computational imaging system is also limited by the “accuracy of the forward mathematical model” and “the reliability of inverse reconstruction algorithm”. Besides, the unpredictability of real physical imaging process and the complexity of solving high dimensional ill-posed inverse problems have become the bottleneck of further development of this field. In recent years, the rapid development of artificial intelligence and deep learning for the technology opens a new door for computational optical imaging technology. Unlike “physical driven” model that traditional computational imaging method is based on, computational imaging based on deep learning is a kind of “data-driven” method, which not only solves many problems considered quite challenge to be solved in this field, but also achieves remarkable improvement in information acquisition ability, imaging functions, and key performance indexes of imaging system, such as spatial resolution, temporal resolution, and detection sensitivity. This review first briefly introduces the current status and the latest progress of deep learning technology in the field of computational optical imaging. Then, the main problems and challenges faced by the current deep learning method in computational optical imaging field are discussed. Finally, the future developments and possible research directions of this field are prospected.
左超, 冯世杰, 张翔宇, 韩静, 陈钱. 深度学习下的计算成像:现状、挑战与未来[J]. 光学学报, 2020, 40(1): 0111003. Chao Zuo, Shijie Feng, Xiangyu Zhang, Jing Han, Chen Qian. Deep Learning Based Computational Imaging: Status, Challenges, and Future[J]. Acta Optica Sinica, 2020, 40(1): 0111003.