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Proxy images recovered when random sensing is used at different sensing rate For the LS CN N algorithm [15], we learned the sampling matrix for the MNIST dataset, i.e. we get the Ψ and Ψ T matrices in Eq. 4 from the first and second fully connected
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Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the si...
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... examples of proxy images recovered with the trans- pose of the random Gaussian matrix using Eq. 4 are shown in Figure 4 for four sampling rates. Starting from such a proxy image, we compute CNN features (coefficients of the fully connected last layer) using the Lenet5 model [14]. ...
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