Multi-Modal Super Resolution for Dense Microscopic Particle Size Estimation
This addresses particle size estimation in industries like manufacturing, but it is incremental as it applies existing cGAN methods to a specific domain problem.
The paper tackles low-resolution optical microscope images for particle size analysis by using conditional GANs to super-resolve them to resemble scanning electron microscope images, achieving results benchmarked against human annotators and SEM images.
Particle Size Analysis (PSA) is an important process carried out in a number of industries, which can significantly influence the properties of the final product. A ubiquitous instrument for this purpose is the Optical Microscope (OM). However, OMs are often prone to drawbacks like low resolution, small focal depth, and edge features being masked due to diffraction. We propose a powerful application of a combination of two Conditional Generative Adversarial Networks (cGANs) that Super Resolve OM images to look like Scanning Electron Microscope (SEM) images. We further demonstrate the use of a custom object detection module that can perform efficient PSA of the super-resolved particles on both, densely and sparsely packed images. The PSA results obtained from the super-resolved images have been benchmarked against human annotators, and results obtained from the corresponding SEM images. The proposed models show a generalizable way of multi-modal image translation and super-resolution for accurate particle size estimation.