Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation
Mehmet Yamac, Ugur Akpinar, Erdem Sahin, Serkan Kiranyaz, Moncef, Gabbouj

TL;DR
This paper introduces GTSNET, a learnable tensor summation approach for compressive sensing that unifies various CS methods and improves reconstruction accuracy, especially for multi-dimensional signals like RGB images.
Contribution
The paper proposes a generalized tensor summation model for CS that simplifies learning measurement matrices and enhances reconstruction performance across diverse signal types.
Findings
Outperforms state-of-the-art in lower measurement rates
Significant improvements for RGB image CS
Unified framework for various CS setups
Abstract
In CS literature, the efforts can be divided into two groups: finding a measurement matrix that preserves the compressed information at the maximum level, and finding a reconstruction algorithm for the compressed information. In the traditional CS setup, the measurement matrices are selected as random matrices, and optimization-based iterative solutions are used to recover the signals. However, when we handle large signals, using random matrices become cumbersome especially when it comes to iterative optimization-based solutions. Even though recent deep learning-based solutions boost the reconstruction accuracy performance while speeding up the recovery, still jointly learning the whole measurement matrix is a difficult process. In this work, we introduce a separable multi-linear learning of the CS matrix by representing it as the summation of arbitrary number of tensors. For a special…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Advanced Data Compression Techniques · Indoor and Outdoor Localization Technologies
