Fourier Ring Correlation

Fourier Ring Correlation (FRC) is a quantitative image quality metric that assesses the agreement between two images in the frequency domain by analyzing the correlation of Fourier coefficients at increasing radial distances from the origin. Current research focuses on leveraging FRC for improved image denoising, particularly through the development of novel loss functions for training neural networks like U-Nets, leading to faster and potentially more effective denoising compared to traditional methods. Furthermore, FRC's application extends beyond microscopy to broader image analysis, and its integration into Bayesian inference frameworks promises to enhance the accuracy and resolution of structural determination in fields like cryo-electron microscopy. This versatile technique offers significant potential for improving image analysis across diverse scientific disciplines.