Empirical Validation of SNR-Dependent Deep Learning Architectures for Label-Free Super-Resolution in Optical...

Optical microscopy is widely used across scientific, industrial, and clinical settings, yet its lateral resolution is fundamentally limited by diffraction (down to ~200 nm in 400 nm visible light). While super-resolution techniques can overcome this barrier, most existing approaches rely on specialised hardware or fluorescent labelling, restricting their accessibility in routine microscopy workflows. As such, past research studies (including our own) have focused on developing deep neural network (DNN) architectures capable of super-resolving optical micrographs. In the present context, two of our previously developed DNN architectures, termed O-Net and Θ-Net, are systematically evaluated using a novel custom-fabricated optical test target containing nanoscale features independently calibrated by atomic force microscopy (AFM). The results indicate that both architectures surpass the diffraction-limited resolution of the optical system employing halogen illumination (peak λ_em = 619.4 nm; λem¯ = 612.2 nm), resolving features down to 170 nm in PCM and 200 nm in DIC (at least). Additionally, the models exemplify a complementary SNR-dependent behaviour-O-Net achieves superior performance under high-SNR conditions, while Θ-Net demonstrates enhanced robustness when SNR is reduced. These findings have important implications in computational super-resolution microscopy, highlighting the potential applicability of the assayed model architectures under differing image acquisition protocols.