Matlab/Python Codes of some selected works

1. S. Mukherjee, M. Carioni, O. Öktem, and C.-B. Schönlieb, "End-to-end reconstruction meets data-driven regularization for inverse problems," arXiv:2106.03538v1. (paper) (python code)

2. S. Mukherjee, O. Öktem,  and C.-B. Schönlieb, "Adversarially learned iterative reconstruction for imaging inverse problems,"  arXiv:2103.16151v1, Mar. 2021. (paper) (python code) 

3. S. Mukherjee, S. Dittmer, Z. Shumaylov, S. Lunz, O. Öktem, and C.-B. Schönlieb, "Learned convex regularizers for inverse problems," arXiv:2008.02839v2, Mar. 2021. (paper) (python code)

4. S. Mukherjee and C. S. Seelamantula, “Phase retrieval from binary measurements,” IEEE Signal Process. Lett., vol. 25, no. 3, pp. 348–352, Mar. 2018. (Matlab)

5. S. Mukherjee, D. Mahapatra, and C. S. Seelamantula, “DNNs for sparse coding and dictionary learning,” accepted to Bayesian Deep Learning Workshop, Neural Info. Process. Systems (NIPS), Dec. 2017. (Python, developed by D. Mahapatra)

6. S. Mukherjee, R. Basu, and C. S. Seelamantula, “L1-K-SVD: A robust dictionary learning algorithm with simultaneous update,” Signal Process. (Elsevier), vol. 123, pp. 42–52, Jun. 2016. (Matlab)

7. S. Mukherjee and C. S. Seelamantula, “Fienup algorithm with sparsity constraints: Application to frequency-domain optical-coherence tomography,” IEEE Trans. Signal Process., vol. 62, no. 18, pp. 4659–4672, Sep. 2014. (Matlab)