Image Processing Toolbox

Image Enhancement

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.

The toolbox includes specialized filtering routines and a generalized multidimensional filtering function that handles integer image types, offers multiple boundary-padding options, and performs convolution and correlation.

Using predefined filters and functions you can:

Enhancing Multispectral Color Composite Images
Constructing color composites to highlight and segment vegetation in satellite imagery.

Morphological Operators

Morphological operators enable you to enhance contrast, remove noise, thin regions, or perform skeletonization on regions. Morphological functions in Image Processing Toolbox include:

Texture segmentation using texture filters
Texture Segmentation Using Texture Filters
Identifying regions of different textures using entropy measurements and morphological operations.

Image Deblurring

Image deblurring algorithms in Image Processing Toolbox include 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.

Deblurring Images Using the Blind Deconvolution Algorithm
Restoring an image when no information about the distortion is available.
Next: Image Analysis

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