Performance Comparison Index for Image Super-Resolution Models

Abstract

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 article introduces a technique for comparing super-resolution models using a pattern-based approach. The proposed method 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.