The problem associated with compressive sensing using l1 is that for large-size images we can not continue with the iterations.
If anybody has a solution or the code for that let me know.
Dear Vivek Upadhyaya
These articles might be helpful, have a look:
1. https://www.osapublishing.org/DirectPDFAccess/7BF89A9D-E921-4ABD-869BDD7E72D5C8D3_402841/boe-10-1-1.pdf?da=1&id=402841&seq=0&mobile=no
2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474767/
Kind Regards
Qamar Ul Islam
Dear Vivek Upadhyaya ,
look at chapter three(SPARSITY AND COMPRESSED SENSING) of bellow book included with code.
https://databookuw.com/databook.pdf
Iterations are generally inevitable in l1 optimization based image reconstruction, but you can still select and implement computationally efficient sub-routines in the algorithm.
The new CR-Sparse library includes an ADMM based solver for l1-minimization. It is built using Google JAX and hence can be run on GPUs and TPUs too.
You may check out a tutorial here https://cr-sparse.readthedocs.io/en/latest/tutorials/admm_l1.html. The ADMM solver is designed to work with linear operators. We also provide a collection of linear operator implementations. See here: https://cr-sparse.readthedocs.io/en/latest/source/lop.html
Also see an example of how we deblur an image using FISTA https://cr-sparse.readthedocs.io/en/latest/gallery/lop/deblurring.html#sphx-glr-gallery-lop-deblurring-py .
