Image super-resolution is a critical aspect of image enhancement, facilitating the reconstruction of high-quality images from low-resolution inputs. Traditional quality assessment metrics like SSIM, MSE, and PSNR have limitations in effectively evaluating super-resolution models due to their focus on pixel values and statistical properties, overlooking overall visual quality. This repository introduces a technique for comparing super-resolution models using a pattern-based approach. It evaluates image quality by analyzing the harmonics, providing a performance comparison index that surpasses traditional metrics. By focusing on the frequency domain and magnitudes of Fourier components, this technique effectively captures image features and patterns, enabling a more comprehensive assessment of super-resolution model performance.

The paper is under peer-review, however the preprint version is available to read: https://dx.doi.org/10.21203/rs.3.rs-4444865/v1

@article{kocmarli2024performance,
  title={Performance Comparison Index for Image Super-Resolution Models},
  author={Kocmarli, Gokhan and Esmer, Gokhan Bora},
  year={2024},
  url = {http://dx.doi.org/10.21203/rs.3.rs-4444865/v1},
  doi = {10.21203/rs.3.rs-4444865/v1},
}