Plug-and-play (PnP) is a recent paradigm for signal reconstruction from incomplete noisy measurements that has shown lot of promise. PnP consists of repeatedly inverting a forward model followed by denoising; a powerful denoiser is used which acts as a regularizer. In recent work , , we prove that for a class of linear denoisers it is possible to come up with theoretical guarantees on exact and stable recovery of the ground-truth signal. In particular, for compressed sensing using random matrices, we are able to work out the sample complexity for exact recovery.Faculty: Prof. Kunal Narayan Chaudhury References:  R. G. Gavaskar and K. N. Chaudhury, “Regularization using denoising: Exact and robust signal recovery,” Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2022.  R. G. Gavaskar, C. D. Athalye and K. N. Chaudhury, “Exact and robust compressed sensing using plug-and-play regularization,” submitted to IEEE Transactions on Image Processing.