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Hyperspectrally compressed ultrafast photography (HCUP) based on compressedsensing and the time- and spectrum-to-space mappings can simultaneously realizethe temporal and spectral imaging of non-repeatable or difficult-to-repeattransient events passively in a single exposure. It possesses an incrediblyhigh frame rate of tens of trillions of frames per second and a sequence depthof several hundred, and plays a revolutionary role in single-shot ultrafastoptical imaging. However, due to the ultra-high data compression ratio inducedby the extremely large sequence depth as well as the limited fidelities oftraditional reconstruction algorithms over the reconstruction process, HCUPsuffers from a poor image reconstruction quality and fails to capture finestructures in complex transient scenes. To overcome these restrictions, wepropose a flexible image reconstruction algorithm based on the total variation(TV) and cascaded denoisers (CD) for HCUP, named the TV-CD algorithm. Itapplies the TV denoising model cascaded with several advanced deeplearning-based denoising models in the iterative plug-and-play alternatingdirection method of multipliers framework, which can preserve the imagesmoothness while utilizing the deep denoising networks to obtain more priori,and thus solving the common sparsity representation problem in local similarityand motion compensation. Both simulation and experimental results show that theproposed TV-CD algorithm can effectively improve the image reconstructionaccuracy and quality of HCUP, and further promote the practical applications ofHCUP in capturing high-dimensional complex physical, chemical and biologicalultrafast optical scenes.

A flexible and accurate total variation and cascaded denoisers-based  image reconstruction algorithm for hyperspectrally compressed ultrafast  photography