Hyperspectral image superresolution is a highly attractive topic in computer vision and has
attracted many researchers' attention. However, nearly all the existing methods assume that …

A hyperspectral image (HSI) super-resolution (SR) is a highly attractive topic in computer
vision. However, most existed methods require an auxiliary high-resolution (HR) image with …

Most recent learning-based single-image super-resolution methods first interpolate the low-
resolution (LR) input, from which overlapped LR features are then extracted to reconstruct …

Recent studies show that research on single image super-resolution (SISR) has achieved
great success by using deep convolutional neural network (CNN). Different types of features …

Image super-resolution using self-optimizing mask via fractional-order gradient interpolation
and reconstruction aims to recover detailed information from low-resolution images and …

Motivated by the fact that image patches could be inherently represented by matrices, single-
image super-resolution is treated as a problem of learning regression operators in a matrix …

Sparse coding based-methods show great effectiveness in single image super-resolution
(SR). Existing methods generally use only synthesis sparse coding. However, the analysis …

In this study, a novel single image super‐resolution (SR) method, which uses a generated
dictionary from pairs of high‐resolution (HR) images and their corresponding low‐resolution …

Sparse coding (SC) has recently become a widely used tool in signal and image processing.
The sparse linear combination of elements from an appropriately chosen over‐complete …

In this report, issues that affect the performance of the neighbor embedding (NE)-based
Super-Resolution (SR) method are analyzed. Effective enrichment of the dictionary is a …