We are currently in the midst of an exciting revolution in microscopy. In many ways, this has been happening for several decades, but it is the rate of development of new methods that has increased recently. The last few years have seen an impressive proliferation of new instruments for imaging at higher resolution, imaging single molecules and faster and more sensitive multidimensional live ce...

Super-resolution microscopy is increasingly becoming an important tool for biological research, providing valuable information at the nanometer-length scales inside cells and tissues. In the past decade numerous technological advancements have transformed super-resolution microscopes into powerful tools of discovery. While the first super-resolution images took several hours to acquire, recent ...

The combination of image mosaicing and super-resolution imaging, i.e. superresolution mosaicing, is a powerful means of representing all the information of multiple overlapping images to obtain a high resolution broad view of a scene. In most current image acquisition systems, images are routinely compressed prior to transmission and storage. In this paper, we present a robust super-resolution ...

Depth image super-resolution is an extremely challenging task due to the information loss in sub-sampling. Deep convolutional neural network have been widely applied to color image super-resolution. Quite surprisingly, this success has not been matched to depth super-resolution. This is mainly due to the inherent difference between color and depth images. In this paper, we bridge up the gap and...

Applications in materials and biological imaging are limited by the ability to collect high-resolution data over large areas practical amounts of time. One solution this problem is low-resolution interpolate produce a image. However, most existing super-resolution algorithms designed for natural images, often require aligned pairing high training data, may not directly incorporate model sensor....

In this study, video super-resolution using particle swarm optimization (PSO) is proposed to super-resolve low-resolution (LR) frames. The proposed super-resolution method consists of three main modules, i.e., supersampling, spatio-temporal classification, and frame fusion using PSO. In the proposed method, the LR frames are super-resolved to high-resolution frames through the fusion of four fu...

Super resolution technology origins from the field of image restoration. The increasing difficulties in the resolution improvement by hardware prompts the super resolution reconstruction that can solve this problem effectively, but the general algorithms of super resolution reconstruction model are unable to quickly complete the image processing. Based on this problem, this paper studies on ada...