Super resolution imaging is becoming an increasingly important tool in the arsenal of methods available to cell biologists. In recognition of its potential, the Nobel Prize for chemistry was awarded to three investigators involved in the development of super resolution imaging methods in 2014. The availability of commercial instruments for super resolution imaging has further spurred the develo...

Super-resolution reconstruction produces one or a set of high-resolution images from a sequence of low-resolution frames. This paper reviews a variety of super-resolution methods proposed in the last twenty years, and provides some insight to, and a summary of, our recent contributions to the general super-resolution problem. In the process, a detailed study of several very important aspects of...

This note is a rough summary of the super resolution methods that I recently learned. To begin with, what’s the definition of super resolution? Super Resolution (SR) is a class of techniques that enhance the resolution of an image. For example, given a 256 × 256 image as input, the output of a SR process is a corresponding 512× 512 image. Notice that super resolution is a highly ill-posed probl...

Mosaicing and super resolution are two ways to combine information from multiple frames in video sequences. Mosaicing displays the information of multiple frames in a single panoramic image. Super-resolution uses regions which appear in multiple frames to improve resolution and reduce noise. The aim of this work is constructing a high resolution mosaic from a video sequence in an efficient way....

We present a new technology for super-resolution fluorescence imaging, based on conical diffraction. Conical diffraction is a linear, singular phenomenon, taking place when a laser beam is diffracted through a biaxial crystal. We use conical diffraction in a thin biaxial crystal to generate illumination patterns that are more compact than the classical Gaussian beam, and use them to generate a ...

A robust approach for super resolution is presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion erros, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an iterative process to achieve a super resolut...