Lei, S., Shi, Z. and Zou, Z. (2020) Coupled Adversarial Training for Remote Sensing Image Super-Resolution. IEEE Transactions on Geoscience and Remote Sensing, 58, 3633-3643.
https://doi.org/10.1109/tgrs.2019.2959020

has been cited by the following article:

• TITLE: Super-Resolution Using Fourier Image Transformer

  AUTHORS: Leith Uwaydah

  KEYWORDS: Super Resolution, Image, Fourier Transform, Frequency, Time, Domain

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.13 No.5, May 26, 2025

  ABSTRACT: The primary goal of Super-Resolution (SR) is to reconstruct an image of higher quality from an image of lower quality. The Fourier transform helps in this process by recovering lost, high-frequency details. Various applications, such as satellite imaging, forensics, and surveillance, require high resolution, particularly when zooming in on specific parts of an image. To address this challenge and enhance image reconstruction, this article explores the Fourier transform and the Fourier Inverse transform. We can predict missing high-frequency components, resulting in a higher-resolution output from a low-resolution input. This approach demonstrates the practicality of solving relevant image analysis tasks in Fourier space, a domain that is inherently inaccessible to traditional convolutional architectures.