This repository contains a comprehensive project aimed at improving the analysis of electron diffraction patterns through advanced image processing techniques. The project is divided into two main objectives:
The first aim is to develop a graph model for accurately locating points in electron diffraction patterns. This involves:
- Detecting the center of the image.
- Cropping the image to a 512x512 pixel resolution.
Accurate point detection is crucial for proper analysis of electron diffraction patterns, expediting the process, and reducing human workload.
The second aim is to enhance simulated electron diffraction patterns to make them more representative of real data by:
- Adding synthetic noise.
- Applying blur filters to the simulated patterns.
This enhancement ensures that simulated patterns closely mimic real data, leading to more accurate results when testing image processing algorithms.
Machine learning, statistical analysis, and image processing are some of the data science techniques that will be used. The project will specifically involve creating a graph model for precise point detection, adding synthetic noise to simulated patterns to make them more resemblant of real data, and testing the effectiveness of the image processing algorithms on actual data.