Non-negative sparse coding shrinkage for image denoising using normal inverse Gaussian density model

This paper proposes a novel image denoising technique based on the normal inverse Gaussian (NIG) density model and the extended non-negative sparse coding (NNSC). The NIG density function, which is fully specified by four real-valued parameters, represents a class of flexible closed form distribution and is quite suitable for modeling sparse data. By choosing appropriate parameters, one can describe a variety of data distributions. In this paper, we demonstrate that the NIG density function provides good fit to non-negative sparse data. With the aid of NIG-based maximum a posteriori estimator (MAP), significant denoising can be achieved for non-negatively and sparsely coded images corrupted with additive Gaussian noise. It is also shown that the proposed NNSC shrinkage technique is adaptive to various distribution properties of natural image data. Experimental results confirm the effectiveness of the proposed NIG based NNSC shrinkage method for image denoising. The comparison with other denoising methods are also made and it is shown that the proposed method produces the best denoising effect.