Super Resolution Method for Fluorescent Image Deconvolution

Image Super - Resolution Using Fast Deconvolution

2 Faculty of Computational Mathematics and Cybernetics, Moscow Lomonosov State University, 119991, Russia, Moscow, Leninskie gory, (495) 939-11-29, [email protected] 3 Faculty of Computational Mathematics and Cybernetics, Moscow Lomonosov State University, 119991, Russia, Moscow, Leninskie gory, (495) 939-11-29, [email protected] 4 The institute of Informatics problems of the Russian Academy of sc...

Super-resolution fluorescent methods: where next for super-resolution?

A multi-frame image super-resolution method

Multi-frame image super-resolution (SR) aims to utilize information from a set of lowresolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relation...

Simultaneous super-resolution and blind deconvolution

In many real applications, blur in input low-resolution images is a nuisance, which prevents traditional super-resolution methods from working correctly. This paper presents a unifying approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We introduce a method which assumes no prior information about the shape of degrad...

A Deep Model for Super-resolution Enhancement from a Single Image

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...