Because of sensor malfunction and poor atmospheric conditions, there is usually a great
deal of missing information in optical remote sensing data, which reduces the usage rate …

The emerging field of compressed sensing has potentially powerful implications for the
design of optical imaging devices. In particular, compressed sensing theory suggests that …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

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 …

Simultaneous sparse coding (SSC) or nonlocal image representation has shown great
potential in various low-level vision tasks, leading to several state-of-the-art image …

The employed dictionary plays an important role in sparse representation or sparse coding
based image reconstruction and classification, while learning dictionaries from the training …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …

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 …

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity
models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing …

In this paper, we propose a very simple and elegant patch-based, machine learning
technique for image denoising using the higher order singular value decomposition …