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MATLAB数字图像处理英文文献
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关于应用MATLAB实现数字图像处理的一篇英文版电子图书。适用于做图像处理方面毕业设计等参考
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Rafael C. Gonzalez
University of Tennessee
Richard E. Woods
MedData Interactive
Steven L. Eddins
The MathWorks, Inc.
Upper Saddle River, NJ 07458
Digital Image
Processing
Using MATLAB
®
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© 2004 by Pearson Education, Inc.
Pearson Prentice-Hall
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All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means,
without permission in writing from the publisher.
Pearson Prentice Hall
®
is a trademark of Pearson Education, Inc.
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65
3
Intensity Transformations
and Spatial Filtering
Preview
The term spatial domain refers to the image plane itself, and methods in this cat-
egory are based on direct manipulation of pixels in an image. In this chapter we
focus attention on two important categories of spatial domain processing:
intensity (or gray-level) transformations and spatial filtering. The latter approach
sometimes is referred to as neighborhood processing, or spatial convolution.In
the following sections we develop and illustrate MATLAB formulations repre-
sentative of processing techniques in these two categories. In order to carry a
consistent theme, most of the examples in this chapter are related to image en-
hancement. This is a good way to introduce spatial processing because enhance-
ment is highly intuitive and appealing, especially to beginners in the field. As will
be seen throughout the book, however, these techniques are general in scope and
have uses in numerous other branches of digital image processing.
Background
As noted in the preceding paragraph, spatial domain techniques operate di-
rectly on the pixels of an image. The spatial domain processes discussed in this
chapter are denoted by the expression
where is the input image, is the output (processed) image, and
T is an operator on defined over a specified neighborhood about point
In addition, T can operate on a set of images, such as performing the ad-
dition of K images for noise reduction.
The principal approach for defining spatial neighborhoods about a point
is to use a square or rectangular region centered at as Fig. 3.1 shows.
The center of the region is moved from pixel to pixel starting, say, at the top, left
1x, y2,1x, y2
1x, y2.
f,
g1x, y2f1x, y2
g1x, y2 = T3f1x, y24
3.1
66 Chapter 3 ■ Intensity Transformations and Spatial Filtering
y
x
Origin
(x, y)
Image f (x, y)
FIGURE 3.1
A
neighborhood of
size about a
point in an
image.
1x, y2
3 * 3
corner, and, as it moves, it encompasses different neighborhoods. Operator T is
applied at each location to yield the output, g, at that location. Only the
pixels in the neighborhood are used in computing the value of g at .
The remainder of this chapter deals with various implementations of the
preceding equation. Although this equation is simple conceptually, its compu-
tational implementation in MATLAB requires that careful attention be paid
to data classes and value ranges.
Intensity Transformation Functions
The simplest form of the transformation T is when the neighborhood in
Fig. 3.1 is of size (a single pixel). In this case, the value of g at de-
pends only on the intensity of at that point, and T becomes an intensity or
gray-level transformation function. These two terms are used interchangeably,
when dealing with monochrome (i.e., gray-scale) images. When dealing with
color images, the term intensity is used to denote a color image component in
certain color spaces, as described in Chapter 6.
Because they depend only on intensity values, and not explicitly on
intensity transformation functions frequently are written in simplified form as
where r denotes the intensity of and s the intensity of g, both at any corre-
sponding point in the images.
3.2.1 Function imadjust
Function imadjust is the basic IPT tool for intensity transformations of gray-
scale images. It has the syntax
g = imadjust(f, [low_in high_in], [low_out high_out], gamma)
As illustrated in Fig. 3.2, this function maps the intensity values in image f
to new values in g, such that values between low_in and high_in map to
1x, y2
f
s = T1r2
1x, y2,
f
1x, y21 * 1
3.2
1x, y2
1x, y2
imadjust
3.2 ■ Intensity Transformation Functions 67
low_in high_in
low_out
high_out
low_in high_inlow_in high_in
gamma 1 gamma 1 gamma 1
FIGURE 3.2
The
various mappings
available in
function
imadjust.
EXAMPLE 3.1:
Using function
imadjust.
values between low_out and high_out. Values below low_in and above
high_in are clipped; that is, values below low_in map to low_out, and those
above high_in map to high_out. The input image can be of class uint8,
uint16, or double, and the output image has the same class as the input. All
inputs to function imadjust, other than f, are specified as values between 0
and 1, regardless of the class of f. If f is of class uint8, imadjust multiplies
the values supplied by 255 to determine the actual values to use; if f is of class
uint16, the values are multiplied by 65535. Using the empty matrix ([]) for
[low_in high_in] or for [low_out high_out] results in the default values
[0 1]. If high_out is less than low_out, the output intensity is reversed.
Parameter gamma specifies the shape of the curve that maps the intensity
values in f to create g. If gamma is less than 1, the mapping is weighted toward
higher (brighter) output values, as Fig. 3.2(a) shows. If gamma is greater than 1,
the mapping is weighted toward lower (darker) output values. If it is omitted
from the function argument, gamma defaults to 1 (linear mapping).
■ Figure 3.3(a) is a digital mammogram image, f, showing a small lesion, and
Fig. 3.3(b) is the negative image, obtained using the command
>> g1 = imadjust(f, [0 1], [1 0]);
This process, which is the digital equivalent of obtaining a photographic nega-
tive, is particularly useful for enhancing white or gray detail embedded in a
large, predominantly dark region. Note, for example, how much easier it is to
analyze the breast tissue in Fig. 3.3(b). The negative of an image can be ob-
tained also with IPT function imcomplement:
g = imcomplement(f)
Figure 3.3(c) is the result of using the command
>> g2 = imadjust(f, [0.5 0.75], [0 1]);
which expands the gray scale region between 0.5 and 0.75 to the full [0, 1]
range. This type of processing is useful for highlighting an intensity band of
interest. Finally, using the command
>> g3 = imadjust(f, [ ], [ ], 2);
imcomplement
a b c
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