The reconstruction of snapshot compressive imaging (SCI) presents a significant challenge in signal processing. The primary goal of SCI is to employ a low-dimensional sensor to capture high-dimensional data in a compressed form. As a result, compared to traditional compressive sensing, SCI emphasizes capturing structural information and enhancing the reconstruction quality of high-dimensional videos and hyperspectral images. This paper proposes a novel SCI reconstruction method by integrating non-convex regularization approximation in conjunction with rank minimization. Furthermore, we address the characterization of structural information by leveraging nonlocal self-similarity across video frames to improve the reconstruction quality. We also develop an optimization algorithm based on the alternating direction method of multipliers (ADMM) to solve the model and provide a convergence algorithm analysis. Extensive experiments demonstrate that the proposed approach can potentially reconstruct SCI effectively.

中文翻译：

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用于视频快照压缩成像的非凸低秩正则化方法


快照压缩成像 （SCI） 的重建在信号处理中提出了重大挑战。SCI 的主要目标是采用低维传感器以压缩形式捕获高维数据。因此，与传统的压缩传感相比，SCI 强调捕获结构信息并提高高维视频和高光谱图像的重建质量。本文提出了一种新的 SCI 重建方法，将非凸正则化近似与秩最小化相结合。此外，我们通过利用视频帧之间的非局部自相似性来提高重建质量，从而解决结构信息的表征问题。我们还开发了一种基于交替方向乘子法 （ADMM） 的优化算法来求解模型并提供收敛算法分析。大量实验表明，所提出的方法有可能有效地重建 SCI。