Compressed sensing (CS) is a signal processing framework that effectively recovers a signal from a small number of samples. Traditional compressed sensing algorithms, such as basis pursuit (BP) and orthogonal matching pursuit (OMP) have several drawbacks, such as low reconstruction performance at small compressed sensing rates and high time complexity. Recently, researchers focus on deep learning to get compressed sensing matrix and reconstruction operations collectively. However, they failed to consider sparsity in their neural networks to compressed sensing recovery; thus, the reconstruction performances are still unsatisfied. In this paper, we use 2D-discrete cosine transform and 2D-discrete wavelet transform to impose sparsity of recovered signals to deep learning in video frame compressed sensing. We find the reconstruction performance is significantly enhanced.
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Deep Learning for Block-Level Compressive Video Sensing
Semantic Scholar · Computer Science · 2020
Abstract
Compressed sensing (CS) is a signal processing framework that effectively recovers a signal from a small number of samples. Traditional compressed sensing algorithms, such as basis pursuit (BP) and orthogonal matching pursuit (OMP) have several drawbacks, such as low reconstruction performance at small compressed sensing rates and high time complexity. Recently, researchers focus on deep learning to get compressed sensing matrix and reconstruction operations collectively. However, they failed to consider sparsity in their neural networks to compressed sensing recovery; thus, the reconstruction performances are still unsatisfied. In this paper, we use 2D-discrete cosine transform and 2D-discrete wavelet transform to impose sparsity of recovered signals to deep learning in video frame compressed sensing. We find the reconstruction performance is significantly enhanced.