The central aim of Super-Resolution (SR) is to generate a higher resolution image from lower resolution images. High resolution image offers a high pixel density and thereby more details about the original scene. The need for high resolution is common in computer vision applications for better performance in pattern recognition and analysis of images. High resolution is of importance in medical...

This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks...

Super-resolution (SR) aims to overcome the ill-posed conditions of image acquisition. SR facilitates scene recognition from low-resolution image(s). Generally assumes that high and low resolution images share similar intrinsic geometries. Various approaches have tried to aggregate the informative details of multiple low-resolution images into a high-resolution one. In this paper, we present a n...

The Super-Resolution (SR) or High Resolution Image reconstructed from noisy, blurred and aliasing the low resolution image using techniques known as super-resolution reconstruction.The super-resolution reconstruction process to combining the low resoultion images to form high resolution images using the algorithm (i.e interpolation).The super-resolution have phases such as registration,interpol...

We address the super-resolution problem: how to estimate missing high spatial frequency components of a static image. From a training set of fulland lowresolution images, we build a database of patches of corrsponding highand low-frequency image information. Given a new low-resolution image to enhance, we select from the training data a set of 10 candidate high-frequency patches for each patch ...

Existing approaches to super-resolution are not applicable to videos of faces because faces are non-planar, non-rigid, non-lambertian, and are subject to self occlusion. We present super-resolution optical ow as a solution to these problems. Super-resolution optical ow takes as input a conventional video stream, and simultaneously computes both optical ow and a super-resolution version of the e...

The goal of multi-frame image super-resolution is to use information from low-resolution images to construct highresolution images. Current multi-frame image super-resolution methods are highly sensitive to prominent large scale artifacts found within the low-resolution images, leading to reduced image quality. This paper presents a novel adaptive approach to large scale artifact reduction in m...

In this review, we introduce the principles of spatial resolution improvement in super-resolution microscopies that were recently developed. These super-resolution techniques utilize the interaction of light and fluorescent probes in order to break the diffraction barrier that limits spatial resolution. The imaging property of each super-resolution technique is also compared with the correspond...