一种基于全变分和级联去噪器的灵活精确图像重建算法，用于高光谱压缩超快摄影
A flexible and accurate total variation and cascaded denoisers-based image reconstruction algorithm for hyperspectrally compressed ultrafast photography
AI总结：
针对高光谱压缩超快摄影重建质量差的问题，提出基于全变分与级联去噪器的TV-CD算法，在即插即用ADMM框架中融合深度去噪先验，提升重建精度与质量，促进实际应用。
摘要：
基于压缩感知以及时间-光谱到空间映射的高光谱压缩超快摄影（HCUP）能够通过单次曝光被动地实现对不可重复或难以重复的瞬态事件的时间与光谱成像。它具有每秒数十万亿帧的极高帧率和数百帧的序列深度，在单次超快光学成像中发挥着革命性作用。然而，由于极大序列深度导致的数据超高压缩比，以及传统重建算法在重建过程中保真度有限，HCUP面临图像重建质量差的问题，无法捕捉复杂瞬态场景中的精细结构。为克服这些限制，我们提出了一种基于全变分（TV）和级联去噪器（CD）的灵活图像重建算法，命名为TV-CD算法。该算法在迭代式即插即用交替方向乘子法框架中，将全变分去噪模型与多个先进的基于深度学习的去噪模型级联，既能在利用深度去噪网络获取更多先验信息的同时保持图像平滑性，从而解决局部相似性和运动补偿中的常见稀疏表示问题。仿真和实验结果表明，所提出的TV-CD算法能有效提高HCUP的图像重建精度和质量，并进一步促进HCUP在捕获高维复杂物理、化学和生物超快光学场景中的实际应用。
英文摘要：
Hyperspectrally compressed ultrafast photography (HCUP) based on compressed sensing and the time- and spectrum-to-space mappings can simultaneously realize the temporal and spectral imaging of non-repeatable or difficult-to-repeat transient events passively in a single exposure. It possesses an incredibly high frame rate of tens of trillions of frames per second and a sequence depth of several hundred, and plays a revolutionary role in single-shot ultrafast optical imaging. However, due to the ultra-high data compression ratio induced by the extremely large sequence depth as well as the limited fidelities of traditional reconstruction algorithms over the reconstruction process, HCUP suffers from a poor image reconstruction quality and fails to capture fine structures in complex transient scenes. To overcome these restrictions, we propose a flexible image reconstruction algorithm based on the total variation (TV) and cascaded denoisers (CD) for HCUP, named the TV-CD algorithm. It applies the TV denoising model cascaded with several advanced deep learning-based denoising models in the iterative plug-and-play alternating direction method of multipliers framework, which can preserve the image smoothness while utilizing the deep denoising networks to obtain more priori, and thus solving the common sparsity representation problem in local similarity and motion compensation. Both simulation and experimental results show that the proposed TV-CD algorithm can effectively improve the image reconstruction accuracy and quality of HCUP, and further promote the practical applications of HCUP in capturing high-dimensional complex physical, chemical and biological ultrafast optical scenes.
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