Education
Welcome to the education page providing a comprehensive full stack guide for super-resolution microscopy. Here, you will learn about the obvious super-resolution techniques but even more about technical aspects of microscopy, including data storage, image file types, signal theory, computer-aided design (CAD) and of course the necessities from the the vast field of optics. Our goal is to equip you with the knowledge and skills necessary to fully understand and utilize super-resolution techniques in your research or professional endeavors. Whether you are a beginner or have some experience with microscopy, our site offers a wide range of resources to help you expand your understanding and improve your skills.
Optical microscopy
Super-resolution
Signals and systems
Arduino
Computational microscopy
Python
Python is a high-level programming language that is widely used in scientific computing, data analysis, and artificial intelligence. It is known for its simplicity, readability, and versatility, making it a popular choice for many applications. In the field of microscopy and image data analysis, Python is used to process, analyze, and visualize large sets of image data. Python libraries such as NumPy, SciPy, and scikit-image provide powerful tools for image processing and analysis, including image filtering, thresholding, segmentation, and feature extraction. Additionally, libraries such as OpenCV, ITK, and SimpleITK provide interfaces to popular image file formats and imaging libraries. Python’s visualization libraries such as Matplotlib and Seaborn also provide a wide range of options to visualize the data and help to understand the data. Python’s flexibility also allows for easy integration with other software tools and platforms, such as ImageJ and MATLAB. In addition, Python’s active community of developers and researchers constantly contribute new libraries and tools, making it an ever-evolving and rich ecosystem for image data analysis.
Managing environments
Java / ImageJ
Image data analysis
There seems to be a near-infinite amount of information about image analysis with novel machine learning methods adding to it on a perpetual basis. We can only give a small introduction and guide you to useful resources. A great resource for starters is the https://bioimagebook.github.io/ book, a github-hosted, jupyter-notebook based intro the topic for beginners. However, actually doing bioimage analysis requires software. And the same concepts can surface in different software in different ways.
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