Name
Abstract | ||
|---|---|---|
Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer j... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LSP.2018.2879184 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Training,Decoding,Signal processing algorithms,Semantics,Matlab,Shape | Absolute phase,Signal processing,Pattern recognition,Segmentation,Convolutional neural network,Artificial intelligence,Pixel,Encoder,Deep learning,Decoding methods,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 1 | 1070-9908 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. E. Spoorthi | 1 | 0 | 0.34 |
| G. R. K. S. Subrahmanyam | 2 | 20 | 7.13 |
| Rama Krishna Sai Subrahmanyam Gorthi | 3 | 2 | 2.05 |