Multi-Modal Super Resolution for Dense Microscopic Particle Size Estimation

TL;DR

This paper introduces a multi-modal super-resolution approach using cGANs to enhance optical microscope images to SEM quality, enabling more accurate particle size analysis in dense microscopic images.

Contribution

It presents a novel combination of cGANs for super-resolving OM images to SEM quality and a custom detection module for efficient particle size analysis.

Findings

Super-resolved images closely match SEM images in quality.

The method improves particle size estimation accuracy.

Results outperform human annotations in some cases.

Abstract

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.