Image exploration

Last updated on 2025-10-15 | Edit this page

Overview

Questions

  • Question 1

Objectives

  • Create and visualize a histogram with Matplotlib.

  • Identify common problems with image quality by looking at histograms (saturation, clipping, dynamic range).

  • Explain the relationship between pixel intensity values, color maps (LUT) and physical fluorescence channels.

  • Describe how adjusting image display settings, including brightness, contrast, applying color maps, and windowing, affects the data.

  • Distinguish between linear and non-linear adjustments such as gamma correction.

  • Explain the importance of slicing, subsampling, and projections in image analysis.

  • Use image coordinates to access individual values and slices of NumPy arrays.

  • Select a subset of time frames of NumPy arrays using slicing.

  • Generate simple projections of NumPy arrays (max, time).

  • Visualize a single 2D NumPy array with Matplotlib.

  • Display images using different Matplotlib colormaps (i.e. LUTs) and contrast settings.

  • Display several images next to each other with Matplotlib subplots.

  • Add a colorbar to a displayed image with Matplotlib.

  • Open a Napari window and add one or more (multi-dimensional) images to it.

This is a new episode that didn’t exist in the original curriculum

It could take content both from 04-drawing.md and 05-creating-histograms.md.