Enhancing Imaging Generation through Implicit Neural Representations and HyperNetwork for Spatial Variability

Collecting data from diverse perspectives is essential across many fields to achieve high-resolution imaging, from Synthetic Aperture Imaging (SAI) to advanced microscopy techniques. However, due to persistent challenges, fully capturing variability in position, angle, and scale remains difficult, driving the need for advanced data generation techniques. In this paper, we utilise a combination of Implicit Neural Representations and HyperNetwork framework to generate datasets enriched with spatial variability, including variations in position, angle, and scale. This method enhances the diversity of generated data, enabling more flexible and robust image datasets applicable to various imaging tasks. Our approach is evaluated on the MNIST and cryo-EM datasets, where we demonstrate the generation of spatially diverse images that maintain high-quality attributes despite changes in spatial configuration. This work highlights the potential to improve data generation processes, leading to more versatile and comprehensive datasets for scientific use.