Removing noise from the original signal is still a challenging problem for researchers. There
have been several published algorithms and each approach has its assumptions …

In this paper, we propose a denoising method motivated by our previous analysis of the
performance bounds for image denoising. Insights from that study are used here to derive a …

In recent years, sparse coding has been widely used in many applications ranging from
image processing to pattern recognition. Most existing sparse coding based applications …

The image nonlocal self-similarity (NSS) prior refers to the fact that a local patch often has
many nonlocal similar patches to it across the image and has been widely applied in many …

This paper proposes a novel sparse representation model called centralized sparse
representation (CSR) for image restoration tasks. In order for faithful image reconstruction, it …

Most existing state-of-the-art image denoising algorithms are based on exploiting similarity
between a relatively modest number of patches. These patch-based methods are strictly …

This paper proposes a novel method for MRI denoising that exploits both the sparseness
and self-similarity properties of the MR images. The proposed method is a two-stage …

Joint sparse representation (JSR) has shown great potential in various image processing
and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this …

Digital images are matrices of equally spaced pixels, each containing a photon count. This
photon count is a stochastic process due to the quantum nature of light. It follows that all …

Mixed noise removal from natural images is a challenging task since the noise distribution
usually does not have a parametric model and has a heavy tail. One typical kind of mixed …