What is Super Resolution in the Context of Windowing?

1. What is super-resolution method?
2. What is super-resolution radar?
3. What is super-resolution deep learning?
4. How does super-resolution imaging work?

What is super-resolution method?

Super-resolution is based on the idea that a combination of low resolution (noisy) sequence of images of a scene can be used to generate a high resolution image or image sequence. Thus it attempts to reconstruct the original scene image with high resolution given a set of observed images at lower resolution.

What is super-resolution radar?

The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L 1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem.

What is super-resolution deep learning?

Abstract—Single image super-resolution (SISR) is a notori- ously challenging ill-posed problem that aims to obtain a high- resolution (HR) output from one of its low-resolution (LR) versions. Recently, powerful deep learning algorithms have been applied to SISR and have achieved state-of-the-art performance.

How does super-resolution imaging work?

SOFI exploits the correlation of light from individual fluorophores, the more light and the more correlated the light that hits a pixel, the brighter that pixel will be in the SOFI image. By correlating fluorophores in time and/or space, SOFI is a powerful super-resolution technique.