  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 framework 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.
