Generative adversarial network-based super-resolution reconstruction for nanomaterial microscopy images: A review

Super-resolution reconstruction has become an important computational strategy for improving the spatial detail of microscopy images in nanomaterials research, especially when hardware resolution is limited by beam damage, acquisition speed, signal-to-noise tradeoffs, or instrument cost. Among available learning-based approaches, generative adversarial networks (GANs) are attractive because they can recover visually realistic high-frequency textures beyond what is typically achieved by pixel-wise optimization alone. However, the same adversarial mechanisms that improve perceptual sharpness can also introduce hallucinated structures, which is a particularly serious concern when microscopy images are used for morphology analysis, defect interpretation, crystal-feature identification, or quantitative materials characterization. This review summarizes the development of GAN-based super-resolution methods for nanomaterial microscopy images, with an emphasis on imaging modalities, degradation assumptions, model design, evaluation protocols, quantitative performance, and scientific reliability. We first discuss the imaging characteristics and resolution bottlenecks of nanomaterial microscopy, then review the evolution from conventional and CNN-based super-resolution to GAN-driven frameworks. We next compare representative applications across electron and related microscopy settings, highlighting dataset construction strategies, paired versus unpaired learning, and tradeoffs among perceptual quality, fidelity, and generalization. Finally, we discuss major challenges, including domain shift, scarce annotations, weak benchmarking standards, interpretability, and the risk of generating scientifically misleading microstructural details, and we outline future directions for trustworthy and physics-aware super-resolution in nanomaterials imaging. In practical terms, the reviewed approaches are significant because they can expand usable field of view, reduce acquisition burden, and preserve nanoscale information when they are paired with rigorous, task-oriented validation.