Sparse Code Shrinkage Based on the Normal Inverse Gaussian Density Model
نویسندگان
چکیده
In this paper we introduce the recent normal inverse Gaussian (NIG) probability density as a new model for sparsely coded data. The NIG density is a flexible, four-parameter density, which is highly suitable for modeling unimodal super-Gaussian data. We demonstrate that the NIG density provides a very good fit to the sparsely coded data, obtained here via an independent component analysis (ICA) transform of the observations. In image denoising, we utilize this new density by developing a NIG-based maximum a posteriori estimator of a sparsely coded image corrupted by white Gaussian noise. The estimator acts as a shrinkage operator on the noisy components in the sparse domain. We demonstrate the technique by an image denoising experiment.
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