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Filters: Author is SenthilKumar, V. M.  [Clear All Filters]
Sekar, K., Devi, K. Suganya, Srinivasan, P., SenthilKumar, V. M..  2020.  Deep Wavelet Architecture for Compressive sensing Recovery. 2020 Seventh International Conference on Information Technology Trends (ITT). :185–189.
The deep learning-based compressive Sensing (CS) has shown substantial improved performance and in run-time reduction with signal sampling and reconstruction. In most cases, moreover, these techniques suffer from disrupting artefacts or high-frequency contents at low sampling ratios. Similarly, this occurs in the multi-resolution sampling method, which further collects more components with lower frequencies. A promising innovation combining CS with convolutionary neural network has eliminated the sparsity constraint yet recovery persists slow. We propose a Deep wavelet based compressive sensing with multi-resolution framework provides better improvement in reconstruction as well as run time. The proposed model demonstrates outstanding quality on test functions over previous approaches.