A Novel Non-negative Shrinkage Technique Based on The Normal Inverse Gaussian Density Model

This paper proposes a novel image denoising technique based on the normal inverse Gaussian (NIG) density model using an extended non-negative sparse coding (NNSC) algorithm. Here, we demonstrate that the NIG density provides a very good fitness to the non-negative sparse data. In denoising process, by exploiting a NIG-based maximum a posteriori estimator (MAP) of an image corrupted by additive Gaussian noise, the noise can be reduced successfully. This shrinkage technique, also referred to as NNSC shrinkage technique, is self-adaptive to the statistical properties of image data. The experimental results show that the NNSC shrinkage approach is indeed efficient and effective in image denoising. In addition, we also compare the effectiveness of the NNSC shrinkage method with methods of standard sparse coding shrinkage, wavelet-based shrinkage and the Wiener filter. The simulating results show that the NNSC shrinkage method indeed outperforms the three kinds of denoising approaches mentioned above.