Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Modal Super Resolution for Dense Microscopic Particle Size
  Estimation   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.

33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph