Super-Resolution Using Fourier Image Transformer

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.