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• W3188143861 abstract "The efforts in compressive sensing (CS) literature can be divided into two groups: finding a measurement matrix that preserves the compressed information at its maximum level, and finding a robust reconstruction algorithm. In the traditional CS setup, the measurement matrices are selected as random matrices, and optimization-based iterative solutions are used to recover the signals. Using random matrices when handling large or multi-dimensional signals is cumbersome especially when it comes to iterative optimizations. Recent deep learning-based solutions increase reconstruction accuracy while speeding up recovery, but jointly learning the whole measurement matrix remains challenging. For this reason, state-of-the-art deep learning CS solutions such as convolutional compressive sensing network (CSNET) use block-wise CS schemes to facilitate learning. In this work, we introduce a separable multi-linear learning of the CS matrix by representing the measurement signal as the summation of the arbitrary number of tensors. As compared to block-wise CS, tensorial learning eases blocking artifacts and improves performance, especially at low measurement rates (MRs), such as [Formula: see text]. The software implementation of the proposed network is publicly shared at https://github.com/mehmetyamac/GTSNET." @default.
• W3188143861 created "2021-08-16" @default.
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• W3188143861 date "2023-01-01" @default.
• W3188143861 modified "2023-10-18" @default.
• W3188143861 title "Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation" @default.
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• W3188143861 doi "https://doi.org/10.1109/tip.2023.3318946" @default.
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