Rank Minimization for Snapshot Compressive Imaging

By Yang Liu, Xin Yuan, J. Suo, David J. Brady, Qionghai Dai. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018

426 citations recorded by the source corpus.

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3fe00962a460c960c90a84d346e31362ecb364cc

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Citations in the corpus

Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction (2021, methodology): “We compare our MST with several SOTA HSI reconstruction algorithms, including three model-based methods (TwIST [4], GAP-TV [57], and DeSCI [27]) and six CNNbased methods (λ-net [36], HSSP [49], DNU [50], PnPDIP-HSI [35], TSA-Net [33], and DGSMP [18]).”
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (2022, background): “One way to solve this problem is to develop snapshot compressive imaging (SCI) systems and computational reconstruction algorithms [9, 12, 13, 29, 33, 47, 48, 52, 55, 56, 58, 59, 73, 74, 80] from 2D measurement to 3D HSI cube.”
Snapshot Compressive Imaging: Theory, Algorithms, and Applications (2021, methodology): “It has been proved in [70] that, in the noiseless case, GAP and the ADMM will lead to the same results, while, in the noisy case, the ADMM can produce better outcomes due to its robustness to noise; a geometric explanation has been shown in [36] as well as the convergence results of both the ADMM an”