Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation

摘要

Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely significantly active. Such a representation is closely related to redundancy reduction and independent component analysis, and has some neurophysiological plausibility. In this article, we show how sparse coding can be used for de- noising. Using maximum likelihood estimation of nongaussian variables corrupted by Gaussian noise, we show haw to apply a soft-thresholding (shrinkage) operator on the components of sparse coding so as to reduce noise.