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.
