Traditional patch-based sparse representation modeling of natural images usually suffer
from two problems. First, it has to solve a large-scale optimization problem with high …

Sparse representation models code an image patch as a linear combination of a few atoms
chosen out from an over-complete dictionary, and they have shown promising results in …

As a powerful statistical image modeling technique, sparse representation has been
successfully used in various image restoration applications. The success of sparse …

A family of the block matching 3-D (BM3D) algorithms for various imaging problems has
been recently proposed within the framework of nonlocal patchwise image modeling,. In this …

Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar
patches to construct patch groups, recent studies have revealed that structural sparse …

A general framework for solving image inverse problems with piecewise linear estimations is
introduced in this paper. The approach is based on Gaussian mixture models, which are …

After a decade of extensive study of the sparse representation synthesis model, we can
safely say that this is a mature and stable field, with clear theoretical foundations, and …

We propose a weighted difference of anisotropic and isotropic total variation (TV) as a
regularization for image processing tasks, based on the well-known TV model and natural …

The deep image prior showed that a randomly initialized network with a suitable architecture
can be trained to solve inverse imaging problems by simply optimizing it's parameters to …

Hyperspectral imaging relies on sophisticated acquisition and data processing systems able
to acquire, process, store, and transmit hundreds or thousands of image bands from a given …