Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal
Document Type
Article
Abstract
Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a methodology known as generative adversarial net- work (GAN) which is better at adding texture details to the high resolution output.
Recommended Citation
Song, Yujing
(2019)
"Single Image Super-Resolution,"
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal: Vol. 6:
Iss.
1, Article 9.
DOI: https://doi.org/10.61366/2576-2176.1067
Available at:
https://digitalcommons.morris.umn.edu/horizons/vol6/iss1/9
Primo Type
Article
