DeepAI AI Chat
Log In Sign Up

Deep Blind Compressed Sensing

by   Shikha Singh, et al.

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.


page 1

page 2

page 3

page 4


Dictionary Learning for Blind One Bit Compressed Sensing

This letter proposes a dictionary learning algorithm for blind one bit c...

Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing

Compressed sensing is a powerful tool in applications such as magnetic r...

Hyperspectral Compressive Wavefront Sensing

Presented is a novel way to combine snapshot compressive imaging and lat...

MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval

This paper addresses the general problem of blind echo retrieval, i.e., ...

Blind calibration for compressed sensing: State evolution and an online algorithm

Compressed sensing, allows to acquire compressible signals with a small ...

Compressed sensing with a jackknife and a bootstrap

Compressed sensing proposes to reconstruct more degrees of freedom in a ...

Multi-echo Reconstruction from Partial K-space Scans via Adaptively Learnt Basis

In multi echo imaging, multiple T1/T2 weighted images of the same cross ...