Image Processing Toolbox™ provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, and image registration using deep learning and traditional image processing techniques. The toolbox supports processing of 2D, 3D, and arbitrarily large images.
Image Processing Toolbox apps let you automate common image processing workflows. You can interactively segment image data, compare image registration techniques, and batch-process large datasets. Visualization functions and apps let you explore images, 3D volumes, and videos; adjust contrast; create histograms; and manipulate regions of interest (ROIs).
You can accelerate your algorithms by running them on multicore processors and GPUs. Many toolbox functions support C/C++ code generation for desktop prototyping and embedded vision system deployment.
This example shows how to read an image into the workspace, adjust the contrast in the image, and then write the adjusted image to a file.
This example shows how to automatically detect circular objects in an image and visualize the detected circles.
This example shows how to perform image preprocessing such as morphological opening and contrast adjustment. Then, create a binary image and compute statistics of image foreground objects.
This example shows how to use array arithmetic to process an image with three planes, and plot image data.
Many images are represented by 2-D arrays, where each element stores information about a pixel in the image. Some image arrays have more dimensions to represent color information or an image sequence.
Image types determine how MATLAB® interprets data matrix elements as pixel intensity values. The toolbox supports many image types including binary, grayscale, truecolor, multispectral, and label images.
Learn how image locations are expressed using pixel indices and spatial coordinates.