Hello Najmeh Br.

As a starting point, I think this article can help:

Article A survey on one-bit compressed sensing: theory and applications

Regards.

Thank you @Abdelhak Bougouffa for your guide.

Hello Najmeh Br.,

In 1-bit CS, the limiting case of 1-bit measurements is considered by preserving just the sign information of random samples or measurements, and treating the measurements as sign constraints to be enforced in reconstruction. The original signal is then recovered by constrained optimization to within a scaling factor since sign information does not include amplitude information. To resolve this ambiguity, reconstruction is constrained to be on the unit sphere to reduce the search space.

The following paper is helpful:

«1-Bit Compressive Sensing »

by: P. T. Boufounos and R. G. Baraniuk. 

Best wishes.

Thank you very much Dear Nuha A. S. Alwan

it was helpful.

Best regards

Hello Najmeh Br.,

If you are interested in software codes, an old MATLAB demo of 1-bit CS reconstruction using Binary Iterative Hard Thresholding (BIHT) is available at https://nuit-blanche.blogspot.com/2011/04/cs-robust-1-bit-compressive-sensing.html

The algorithm is described in the paper "Robust 1-Bit Compressive Sensing via

Binary Stable Embeddings of Sparse Vectors" by Laurent Jacques et.al.

I have been working on a Python library called CR-Sparse. It now has a BIHT implementation too.

• API: https://cr-sparse.readthedocs.io/en/latest/source/cs.html#bit-compressive-sensing
• Demo: https://cr-sparse.readthedocs.io/en/latest/gallery/pursuit/cs1bit_biht.html#sphx-glr-gallery-pursuit-cs1bit-biht-py

This Python library works on CPU as well as GPU.

Dear Shailesh Kumar

Thank you very much for your kind and useful answer.

Do you have any experience in using deep eural networks for CS or any other guide in this regard?

Here is a list of papers and code for deep learning based compressive sensing: https://github.com/ngcthuong/Reproducible-Deep-Compressive-Sensing

I had personally coded a few networks.

you can see the code at: https://github.com/carnotresearch/cr-vision/tree/master/src/cr/vision/dl/nets/cs

Here is some docs I wrote explaining a fully convolutional compressive stacked denoising autoencoder: https://cr-vision.carnotresearch.com/srrcs/sda/index.html