Deep Blind Compressed Sensing [article]

Shikha Singh, Vanika Singhal, Angshul Majumdar
2016 arXiv   pre-print
This work addresses the problem of extracting deeply learned features directly from compressive measurements. There has been no work in this area. Existing deep learning tools only give good results when applied on the full signal, that too usually after preprocessing. These techniques require the signal to be reconstructed first. In this work we show that by learning directly from the compressed domain, considerably better results can be obtained. This work extends the recently proposed
more » ... rk of deep matrix factorization in combination with blind compressed sensing; hence the term deep blind compressed sensing. Simulation experiments have been carried out on imaging via single pixel camera, under-sampled biomedical signals, arising in wireless body area network and compressive hyperspectral imaging. In all cases, the superiority of our proposed deep blind compressed sensing can be envisaged.
arXiv:1612.07453v1 fatcat:xcctnv4bbfbbfmfmuotrs4lu6i