| Title | Deep Wavelet Architecture for Compressive sensing Recovery |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Sekar, K., Devi, K. Suganya, Srinivasan, P., SenthilKumar, V. M. |
| Conference Name | 2020 Seventh International Conference on Information Technology Trends (ITT) |
| Keywords | composability, compressed sensing, compressive sampling, convolution, Cyber-physical systems, Deep Compressive Sensing, Deep Learning, discrete wavelet transforms, Image reconstruction, image restoration, information technology, Market research, Multi-resolution, privacy, pubcrawl, Resiliency |
| Abstract | 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. |
| DOI | 10.1109/ITT51279.2020.9320773 |
| Citation Key | sekar_deep_2020 |
Deep Wavelet Architecture for Compressive sensing Recovery