Single-shot spectral-volumetric compressed ultrafast photography

Abstract

In ultrafast optical imaging, it is critical to obtain the spatial structure, temporal evolution, and spectral composition of the object with snapshots in order to better observe and understand unrepeatable or irreversible dynamic scenes. However, so far, there are no ultrafast optical imaging techniques that can simultaneously capture the spatial-temporal-spectral five-dimensional (5D) information of dynamic scenes. To break the limitation of the existing techniques in imaging dimensions, we develop a spectral-volumetric compressed ultrafast photography (SV-CUP) technique. In our SV-CUP, the spatial resolutions in the x , y and z directions are, respectively, 0.39, 0.35, and 3 mm with an 8.8 mm × 6.3 mm field of view, the temporal frame interval is 2 ps, and the spectral frame interval is 1.72 nm. To demonstrate the excellent performance of our SV-CUP in spatial-temporal-spectral 5D imaging, we successfully measure the spectrally resolved photoluminescent dynamics of a 3D mannequin coated with CdSe quantum dots. Our SV-CUP brings unprecedented detection capabilities to dynamic scenes, which has important application prospects in fundamental research and applied science.

Tutorial on compressed ultrafast photography

“…It is noted that CUP systems of more than two views have been featured in recent progress to further boost image quality. 57 – 66 For example, lossless-encoding CUP contains the time-unsheared view and two complementary time-sheared views. 58 Nonetheless, the formation of these views shares similar data acquisition paradigms as the ones described above and thus is not discussed here.…”

“…Recent developments in CUP have explored high-dimensional ultrafast imaging. To date, several advanced systems—such as multispectral CUP, 120 stereo-polarimetric CUP, 57 and spectral-volumetric CUP 62 —have pushed the overall sensing capability to four dimensions and even five dimensions. In the future, by extending the configuration used in stereo-polarimetric CUP 57 to generate multiple perspectives of the dynamic scene, light-field imaging could be incorporated into CUP.…”

基于免训练自监督式神经网络算法实现压缩超快成像高保真图像重构研究

“…为了防止DIP过拟合, 我们结合PnP设计, 使用 图像降噪算法(例如TV [42] 、FFDNet [43] 和DRUNet [44] )替 DIP、DIP-TV、DIP-FFDNet和DIP-DRUNet外, 还包括 在传统迭代算法中具有最佳重构性能的DeSCI算法 [41] , 量与驱动电流匹配良好, 等离子体的内爆是同步的 [47] . 、光 谱-立体压缩超快成像(spectral-volumetric CUP, SV-CUP) [49] 等多维成像模型, 实现更高维数据的恢复, 扩 大CUP类技术在捕捉复杂超快物理事件方面的应用范 围. 当然, PnP-DIP类方法依然存在进一步提升重构质 量的空间.…”