Image Processing

Image Processing Description Topics

• Image Acquisition, Import, and Export
• Image Processing, Analysis, and Visualization
• Algorithm Development and Application Deployment
• Video and Image Processing System Design
• Geospatial Computing
• Medical Imaging

Image Processing, Analysis, and Visualization

Pre- and Post-Processing Images

The Image Processing Toolbox provides reference-standard algorithms for pre- and post-processing tasks that solve frequent system problems, such as interfering noise, low dynamic range, out-of-focus optics, and the difference in color representation between input and output devices. The toolbox includes:

• Image enhancement algorithms, including histogram equalization, decorrelation stretching, and linear, median, or adaptive filtering
• Deblurring algorithms, including blind, Lucy-Richardson, Wiener, and regularized filter deconvolution, as well as conversions between point spread and optical transfer functions
• Image transforms, including the DCT, Radon, and fan-beam projection
• ICC-compliant color profile import and export
• Color space conversion functions for color spaces such as RGB, sRGB, YCrCb, XYZ, Lab, and HSV

This example illustrates how histogram equalization and median filtering adjust an image’s intensity. The original image on the left has low-contrast, salt and pepper noise, making it hard to discern details. The intensity values of the output image on the right are evenly distributed throughout the range and the noise is reduced.

Analyzing Images

The Image Processing Toolbox provides a comprehensive suite of reference-standard algorithms and graphical tools for image analysis tasks, including:

• Statistical functions, such as calculating the image mean or standard deviation, displaying an image histogram, and plotting a profile of intensity values
• Edge-detection algorithms, including the Sobel, Prewitt, Roberts, Canny, and Laplacian of Gaussian methods
• Image segmentation algorithms, including automatic thresholding, edge-based methods, and morphology-based methods such as the watershed transform
• Morphological operators, such as dilation, erosion, flood-fill, and pixel connectivity
• Hough transform to detect lines and extract line segments from an image
• Feature measurement functions, to measure image properties, such as the area of a specified image region, center of mass, bounding box, surface roughness and color variation

A typical session using MATLAB and the Image Processing Toolbox to perform connected components analysis on an image with nonuniform background intensity.

Visualizing Images

The Image Processing Toolbox provides an integrated environment for interactive image display and exploration. You can load an image from a file or from the MATLAB workspace, adjust the contrast, examine a region of pixels, view image information, zoom and pan around the image, and track your location in a large image using an overview window. The image display environment is modular and open, enabling you to customize the tools provided and add your own.

The Overview window (top left) is used to navigate when looking at magnified views in the Image Tool (center). The Pixel Region window (bottom) superimposes pixel values on a highly magnified view of the image. LANDSAT image of Paris courtesy of Space Imaging, LLC.