Existing super-resolution evaluation systems for fluorescence microscopy images struggle to effectively detect potential artifacts in weak signal reconstruction. This study aims to establish a multi-dimensional evaluation framework that integrates frequency-domain evidence to verify the reliability of super-resolution techniques under low signal-to-noise ratio (SNR) conditions. All ground-truth (HR) images used in this study are experimentally acquired fluorescence microscopy data; the corresponding low-quality inputs are simulated from HR via controlled degradations (e.g., bicubic downsampling and frequency-truncation-based degradation) to enable paired quantitative evaluation. We designed a hierarchical comparative experiment to systematically evaluate the performance differences of CNN (SRCNN/FSRCNN), GAN (Real-ESRGAN), and Transformer (SwinIR) architectures on nucleus and whole-cell structure datasets. This study reveals a significant decoupling between “visual sharpness” and “signal fidelity”: while Real-ESRGAN can generate highly impactful high-frequency textures, its checkerboard effect in the spectrum and random residuals in the error map expose serious “illusion” risks, making it unsuitable for precise quantitative analysis.
From PSNR to Frequency Evidence: Evaluating Super-Resolution Reliability on Low-SNR Fluorescence Channels
