Image Processing Toolbox™ 6.1

Product Description

• Introduction and Key Features
• Importing and Exporting Images
• Pre- and Post-Processing Images
• Analyzing Images
• Displaying and Exploring Images and Video
• Spatial Transformations and Image Registration

Pre- and Post-Processing Images

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.

Enhancing Images

Image enhancement techniques in Image Processing Toolbox enable you to increase the signal-to-noise ratio and accentuate image features by modifying the colors or intensities of an image. You can:

• Perform histogram equalization
• Perform decorrelation stretching
• Remap the dynamic range
• Adjust the gamma value
• Perform linear, median, or adaptive filtering

The toolbox includes specialized filtering routines and a generalized multidimensional filtering function that handles integer image types, multiple boundary padding options, and convolution and correlation. Predefined filters and functions for designing and implementing your own linear filters are also provided.

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

Deblurring Images

Image Processing Toolbox supports several fundamental deblurring algorithms, including blind, Lucy-Richardson, Wiener, and regularized filter deconvolution, as well as conversions between point spread and optical transfer functions. These functions help correct blurring caused by out-of-focus optics, movement by the camera or the subject during image capture, atmospheric conditions, short exposure time, and other factors. All deblurring functions work with multidimensional images.

Image of the sun using deblurring algorithms in Image Processing Toolbox. Image courtesy of the SOHO EIT Consortium.

Managing Device-Independent Color

Image Processing Toolbox enables you to accurately represent color independently from input and output devices. This is useful when analyzing the characteristics of a device, quantitatively measuring color accuracy, or developing algorithms for several different devices. With specialized functions in the toolbox, you can convert images between device-independent color spaces, such as sRGB, XYZ, xyY, L*a*b*, uvL, and L*ch.

For more flexibility and control, the toolbox supports profile-based color space conversions using a color management system based on ICC version 4. For example, you can import n-dimensional ICC color profiles, create new or modify existing ICC color profiles for specific input and output devices, specify the rendering intent, and find all compliant profiles on your machine.

Image Transforms

Transforms such as FFT and DCT play a critical role in many image processing tasks, including image enhancement, analysis, restoration, and compression. Image Processing Toolbox provides several image transforms, including DCT, Radon, and fan-beam projection. You can reconstruct images from parallel-beam and fan-beam projection data (common in tomography applications). Image transforms are also available in MATLAB and in Wavelet Toolbox (available separately).

Image Conversions

Imaging applications often require conversion between data classes and image types. Image Processing Toolbox provides a variety of utilities for conversion between data classes, including single- and double-precision floating-point and signed or unsigned 8-, 16-, and 32-bit integers. The toolbox includes algorithms for conversion between image types, including binary, grayscale, indexed color, and truecolor. Specifically for color images, the toolbox supports a variety of color spaces such as YIQ, HSV, and YCrCb, Bayer pattern encoded, and high dynamic range images.

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