Specifications

Deblurring Images
The 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.
Managing Device-Independent Color
The Image Processing Toolbox enables you
to accurately represent color independently
from input and output devices. This is useful
when developing algorithms for several
different devices or when analyzing the char-
acteristics of a particular device. Specialized
functions in the toolbox let you:
Convert images between color spaces, such
as RGB, sRGB, YCrCb, XYZ, Lab, and HSV
Import n-dimensional ICC color profiles
to convert images to a device-independent
color space
Create new ICC color profiles for specific
input and output devices
Apply user-defined colormaps to RGB
images to reduce the number of colors
Image Transforms
Transforms such as the FFT and the DCT
play a critical role in many image processing
tasks, including image enhancement, analysis,
restoration, and compression. The Image
Processing Toolbox provides several image
transforms, including the DCT, Radon, and
fan-beam projection. You can use the inverse
Radon transform to reconstruct images from
parallel-beam and fan-beam projection data
(common in tomography applications). Image
transforms are also available in MATLAB and
in the Wavelet Toolbox (available separately).
Analyzing Images
The Image Processing Toolbox provides a
comprehensive suite of reference-standard
algorithms and graphical tools for image
analysis tasks such as statistical analysis, feature
extraction, and property measurement.
Statistical functions
let you analyze the
general characteristics of an image by:
Computing the mean or standard deviation
Determining the intensity values along a
line segment
Displaying an image histogram or plotting a
profile of intensity values
Edge-detection algorithms
let you identify
object boundaries in an image. These algo-
rithms include the Sobel, Prewitt, Roberts,
Canny, and Laplacian of Gaussian methods.
The powerful Canny method can detect true
weak edges without being fooled by noise.
Image segmentation algorithms
determine
Image segmentation algorithms determine Image segmentation algorithms
region boundaries in an image. You can
explore many different approaches to image
segmentation, including automatic thresholding,
edge-based methods, and morphology-based
methods such as the watershed transform, often
used to segment touching objects.
Morphological operators
enable you to
detect edges, enhance contrast, remove noise,
segment an image into regions, thin regions,
or perform skeletonization on regions.
Morphological functions in the Image
Processing Toolbox include:
Hole filling
Peak and valley detection
Watershed segmentation
Reconstruction
Distance transform
The Image Processing Toolbox also contains
advanced image analysis functions that let you:
Measure the properties of a specified image
region, such as the area, center of mass, and
bounding box
Detect lines and extract lines segments from
an image using the Hough transform
Measure properties, such as surface rough-
ness or color variation, using texture
analysis functions
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.