Both non-local (NL) operation and sparse representation are crucial for Single Image Super-
Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non …

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

The performance of any biometric recognition system heavily dependents on finding a good
and suitable feature representation space where observations from different classes are well …

Hyperspectral imaging has many applications from agriculture and astronomy to
surveillance and mineralogy. However, it is often challenging to obtain high-resolution (HR) …

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 …

Multi-focus image fusion has emerged as a major topic in image processing to generate all-
focus images with increased depth-of-field from multi-focus photographs. Different …

Hyperspectral image (HSI) super-resolution, which fuses a low-resolution (LR) HSI with a
high-resolution (HR) multispectral image (MSI), has recently attracted much attention. Most …

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

Deep networks have been successfully applied to visual tracking by learning a generic
representation offline from numerous training images. However, the offline training is time …

Hyperspectral image (HSI) denoising is an essential preprocess step to improve the
performance of subsequent applications. For HSI, there is much global and local …