Howtosignalprocessing
- What is super-resolution techniques?
- Where is super-resolution used?
- What is super-resolution deep learning?
- Why use super-resolution?
What is super-resolution techniques?
Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images. As SR has been developed for more than three decades, both multi-frame and single-frame SR have significant applications in our daily life.
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.
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.
Why use super-resolution?
Photo enlargement made easy.
Super Resolution creates a single image with two times the linear resolution. That means the enhanced image will have twice the width and twice the height of the original image, or four times the total pixel count.
