@article {624918,
	title = {MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance},
	journal = {IEEE Transactions on Signal Processing},
	volume = {67},
	number = {17},
	year = {2019},
	abstract = {Sparse coding refers to the pursuit of the sparsest representation of a signal in a typically overcomplete dictionary. From a Bayesian perspective, sparse coding provides a Maximum a Posteriori (MAP) estimate of the unknown vector under a sparse prior. Various nonlinear algorithms are available to approximate the solution of such problems.In this work, we suggest enhancing the performance of sparse coding algorithms by a deliberate and controlled contamination of the input with random noise, a phenomenon known as stochastic resonance. This not only allows for increased performance, but also provides a computationally efficient approximation to the Minimum Mean Square Error (MMSE) estimator, which is ordinarily intractable to compute. We demonstrate our findings empirically and provide a theoretical analysis of our method under several different cases.},
	url = {https://arxiv.org/abs/1806.10171},
	author = {Dror Simon and Jeremias Sulam and Yaniv Romano and Yue M. Lu and Michael Elad}
}
