Super-resolution, the process of obtaining one or more high-resolution images from one or
more low-resolution observations, has been a very attractive research topic over the last two …

Super‐Resolution reconstruction produces one or a set of high‐resolution images from a
sequence of low‐resolution frames. This article reviews a variety of Super‐Resolution …

The shortcomings in commonly used kernel-based super-resolution drive the study of
improved super-resolution algorithms of higher quality. In the past years a wide range of …

Single-image super-resolution is of great importance for vision applications, and numerous
algorithms have been proposed in recent years. Despite the demonstrated success, these …

With the exponential increase in computing power and broad proliferation of digital cameras,
super-resolution imaging is poised to become the next" killer app." The growing interest in …

A new approach toward increasing spatial resolution is required to overcome the limitations
of the sensors and optics manufacturing technology. One promising approach is to use …

Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both …

Nearly all super-resolution algorithms are based on the fundamental constraints that the
super-resolution image should generate low resolution input images when appropriately …

The spatial resolution that represents the number of pixels per unit area in an image is the
principal factor in determining the quality of an image. With the development of image …

Super-resolution reconstruction produces one or a set of high-resolution images from a set
of low-resolution images. In the last two decades, a variety of super-resolution methods have …