1 Chapter 3 Image Enhancement 2 Image Enhancement To process an image so that the result is more suitable than the original image for a specific application. Objective Image enhancement is one of the most interesting and visually appealing areas of image processing. Note (1) images by non-prejudicial information (2) image enhancement in general is a complex image processing system of preconditioning link. 3 Categories of Image Enhancement
Image enhancement approaches fall into two broad categories
Spatial domain refers to the image plane itself and approaches in this category are based on direct manipulation of pixels in an image.
Frequency domain processing techniques are based on modifying the Fourier transform of an image.
4 Main contents of Image enhancement 5 Background I
The simplest form of T is when the neighborhood is of size 1 1 (that is a single pixel).
In this case g depends only on the value of f at (x y) and T becomes a gray-level (also called an intensity or mapping) transformation function of the form
r and s are variables denoting respectively the gray level of f(x y) and g(x y).
6 Background II
33 neighborhood about a point (x y) in an image
7 concept of point operating
The scope operator T for 1 1 that is only the role of a single pixel the output of g (x y) only with the position (x y) the Agencys input f (x y) and point-to-point treatment T operator as a gray-scale transformation function (gray-scale transformation GST) (also known as the intensity map) describes the input and output gray level gray-scale mapping relationship between. Also known as the contrast enhancement contrast stretching or gray-scale transformation. s T (r) r the original image gray level s enhanced gray-scale image T mapping relations.
8 Histogram I
An image histogram is a graphical representation of the number of pixels in an image as a function of their intensity.
9 Histogram II
The vertical axis is the relative number of pixels at each of the 255 tonal values.
The taller hump in the graph the more pixels reside at that particular tonal range.
10 Histogram III A quite good image should have some different tonal values. That means the tonal range of the image should distribute from left to right in the histograms. Simultaneously the histograms both sides will not have overflowed. 11 Histogram IV 12 Histogram Processing - How It Works I
The operation is very simple. The image is scanned in a single pass and a running count of the number of pixels found at each intensity value is kept. This is then used to construct a suitable histogram.
The histogram of an 8-bit image for example can be thought of as a table with 256 entries or bins indexed from 0 to 255. In bin 0 we record the number of times a gray level of 0 occurs in bin 1 we record the number of times a grey level of 1 occurs and so on up to bin 255.
13 Histogram Processing - How It Works II
Assign zero values to all element of the array hf
For all pixels (xy) of the image f increment hf f(xy) by 1 .
14 Another way to get the histogram is to use the C code as following int ij int imgnm int hstnum for(i0iltnumi) hsti0 for(j0jltn j) for(I0iltmi) hstimgjI
15 Histogram equalization (HE)
Transforms the intensity values so that the histogram of the output image approximately
Matches the flat (uniform) histogram
16 Histogram equalization II
As for the discrete case the following formula applies
L number of grey levels in image (e.g. 255)
nj number of times j-th grey level appears in image
n total number of pixels in the image
17 Histogram of the nature of
Image reflects the distribution And visual experience has the consistency A given image histogram only given the corresponding image histogram is not the only Reflect the whole does not reflect the details
18 The use of histogram
(1) of the digitized image to provide a reasonable reference (2) The histogram of the statistical properties easily obtained images of objects of interest to the area. (3) determine the image binarization threshold
19 Digitized image to provide a reasonable reference
Histogram give a visual indicator used to judge the digital image are quantified Whether the rational use of all the gray area permit
A) proper quantification B) can not effectively make use of dynamic range C) exceeds the dynamic range 20 Determine the image binarization threshold Corresponds to the Background section Background image are black gray objects. Background of black pixels produced a histogram on the left peak Objects in the gray-scale histogram produced a peak on the right. As the Objects in a relatively small number of border pixels resulting in two peaks of Between Valley. Choose Valley as the corresponding gray-scale range T to use under Type of image binarization obtained a binary image. Corresponds to the Object part Threshold T 21 Programming method with histogram express int ij int imgnm int hstnum for(i0iltnumi) hsti0 for(j0jltn j) for(I0iltmi) hstimgjI
22 Histogram equalization deal
Histogram equalization refers to a histogram with gray-scale mapping transform so as to achieve the purpose of image enhancement methods
1) transformed to reduce the gray-scale images some details of the disappearance of 2) certain images such as histogram has a peak contrast ratio of treated too unnatural enhancement.
Histogram equalization deal with the central idea is the original image histogram from the more concentrated into a gray zone in all of gray within the scope of uniform distribution. Histogram equalization is a non-linear image stretching re-distribution of image pixel values are within the scope of a certain gray-scale pixel quantity or less the same. Histogram equalization is to a given change in the distribution of the image histogram into a uniform distribution histogram distribution.
24 Figure (b) are changes in function. Figure (c) the result is a similar very good balanced result. Although the balance derived from the image histogram is not flat gray-level reduction but the distribution of Of view than the original image histogram read flat and expanded dynamic range. Therefore the straight side Figure balanced in real terms is to reduce the gray level of image contrast for the expansion. 25 Histogram equalization III 26 Histogram of the provisions of
Histogram equalization to automatically determine the transformation function which seek to produce a uniform output image histogram. Histogram refers to the provisions of the adoption of an image after gray-scale transformation so that it will have a particular histogram graph form such as the image with a standard image have the same histogram or the image has a specific function form of histogram
27 Histogram equalization IV
28 Histogram equalization V
cumulative histogram 29 Histogram equalization VI
30 Histogram equalization VII
31 Histogram equalization VIII Histogram can also be taken on a part of the image 32 Histogram specification (HS)
An images histogram is transformed according to a desired function
Transforming the intensity values so that the histogram of the output image approximately matches a specified histogram.
33 Histogram specification II histogram1 histogram2 S-1T S T 34 Histogram specification III
35 Gray-scale transformation
Gray-scale image enhancement transformation is another means it will enable increased dynamic range images are the expansion of image contrast image more clearly the characteristics become more pronounced. Gray-scale transformation of its substance is to amend certain rules according to each image pixel gray-scale thus changing the image of the dynamic range. Gray-scale transformation including a linear gray-scale transformation piecewise linear gray-scale transformation such as non-linear gray-scale transformation.
36 Gray Level Transformations II OK cancel overload store smooth auto preview input output 37 Linear gray-scale transformation The assumption that the image f (x y) of gray-scale range of a b Linear transformation images g (x y) the range of a b 38 Histogram specification IV 39 Gray Level Transformations
Contrast stretching is a simple image enhancement technique that attempts to improve the contrast in an image by stretching the range of intensity values it contains to span desired range of values .
Gray Level Transformations concludes linear contrast stretch Piecewise Linear Contrast Stretch Nonlinear contrast stretch .
40 Gray Level Transformations I sr sT(r) 41 Linear contrast stretch I 42 Linear gray-scale transformation 43 Linear contrast stretch II 44 Linear contrast stretch III 255 45 Piecewise Linear Contrast Stretch 46 Nonlinear contrast stretch I
The general form of the log transformations is
The general form of the Index transformations is
47 Non-linear gray-scale transformation
Logarithmic transform the general expression for
Exponential transform the general expression for
When the hope that the image of the low gray area greater tensile and compression of high-gray-zone the This can transform which enables the image gray distribution and characteristics of human visual match. This transformation can be the high gray-scale image gives a much greater tensile zone. 48 Nonlinear contrast stretch II
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