Visible to the public Deep Wavelet Architecture for Compressive sensing Recovery

TitleDeep Wavelet Architecture for Compressive sensing Recovery
Publication TypeConference Paper
Year of Publication2020
AuthorsSekar, K., Devi, K. Suganya, Srinivasan, P., SenthilKumar, V. M.
Conference Name2020 Seventh International Conference on Information Technology Trends (ITT)
Keywordscomposability, 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
AbstractThe 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.
Citation Keysekar_deep_2020