Comparison of spectrum-based image quality assessment metrics

In the field of digital holography, super-resolution techniques play a critical role in enhancing the quality of object reconstruction from low-resolution holograms, thereby addressing limitations imposed by optical systems and sensor resolutions. However, conventional image quality assessment metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) often prove inadequate, especially in sensitive application areas such as medical imaging, due to their limited alignment with human visual perception and their insensitivity to frequency-domain characteristics. To overcome these shortcomings, frequency-domain-based evaluation methods have gained increasing attention. In this study, two metrics grounded in Fourier analysis, the Harmonic Radius Index (HRI) and the Magnitude Knowledge Accuracy (MKA), are investigated and compared for their effectiveness in evaluating super-resolved holographic reconstructions.