Why would super resolution using deep learning beat the old school techniques?

1. What is super resolution deep learning?
2. What is the purpose of super resolution?
3. What technology allows the upgradation of resolution using deep learning algorithms?
4. Where is super resolution used?

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.

What is the purpose of super resolution?

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.

What technology allows the upgradation of resolution using deep learning algorithms?

GANs for Super resolution

Most deep learning based super resolution model are trained using Generative Adversarial Networks (GANs).

Where is super resolution used?

Image Super Resolution refers to the task of enhancing the resolution of an image from low-resolution (LR) to high (HR). It is popularly used in the following applications: Surveillance: to detect, identify, and perform facial recognition on low-resolution images obtained from security cameras.