Color image processing: pseudocolor processing

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C

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image processing:

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pseudocolor processing

Spring 2008 ELEN 4304/5365 DIP 1

by Gleb V. Tcheslavski: [email protected] http://ee.lamar.edu/gleb/dip/index.htm

Preliminaries

Pseudocolor

(false color) image processing consists of

assigning colors to gray values based on a specified

criterion. The term “pseudocolor” emphasizes that the

colors were assigned artificially opposing to the true (real)

colors.

The principal use of pseudocolors is for human

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visualization and interpretation of gray scale details on an

image or their sequence.

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Intensity slicing

The technique of intensity(density) slicingcan be explained by interpreting a grayscale image as a 3D function being sliced by a plane parallel to the coordinate plane of the image.

plane parallel to the coordinate plane of the image. For instance, a plane at l_islices

the image into two levels. Assigning next one color to the pixels, whose intensities are above the plane and another color to the pixels whose

color to the pixels, whose intensities are below the plane (pixels are on different sides of the plane), we create a two-color image, whose appearance

depends on pixel intensities.

Intensity slicing

Alternatively, the same mapping can be interpreted using the following representation.

Any input intensity level is assigned to one of two colors, depending on whether it is above or below the value of l_i. When more levels are used, the mapping function looks like stairs.

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Intensity slicing

In general, this technique is as follows:

Supposing that the image has the gray scale values [0, L-1] where the level l₀represents black [f(x,y) = 0] and the level l_L-1represents white, we form Pplanes perpendicular to the intensity axes at levels

l₁, l₂,..., l_Psuch that 0 < P< L-1 and the planes partition the gray scale into P+ 1 intervals V₁, V₂,…, V_P+1. Then, intensity to color assignment is made according to

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_k

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_k

f x y

=

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if f x y

∈

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( , )

_k

( , )

_k

f x y

c

if f x y

∈

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Where c_kis the color associated with the kth_{intensity interval}_V k

defined by the partitioning planes at l= k-1and l = k.

Intensity slicing

Monochrome image of Pi k Result of intensity li i i t 8 Picker Thyroid Phantom slicing into 8 colored regions

It is quite evident that the regions appearing as uniform (with a constant intensity) in the monochrome image are really quite

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Intensity slicing

Intensity slicing takes much useful and meaningful role when subdivision is based

h i l h t i ti f th i on physical characteristics of the image. In X-ray images of the weld, it is known that, while encountering porosity or a crack in the weld, the full strength of the X-rays would hit the sensor. Therefore, assuming 8-bit X-ray weld images, the intensity

values close to 255 would indicate problems. Therefore, assigning one color to level 255 and another color to all other levels, would simplify the weld inspection and lower its error rate.

Intensity slicing

Average rainfall measurements are

Rain fall data plotted on a usually done by

satellites: a gray-scale image is formed, whose intensity values are proportional to

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world map

precipitation.

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Intensity to color transformation

Other types of intensity-to-color transformations exist.

One practically attractive method implies performing three

independent transformations on the intensity of any input pixel. The results are fed separately into the red green and blue monitor

the red, green, and blue monitor channels producing a composite image whose colors are

modulated by the transformation functions.

Note that the result is a function of pixel’s intensity but not of its position.

Intensity to color transformation

Airport X-ray scanner: ordinary luggage and one with a block of simulated plastic explosives. simulated plastic explosives. Pseudocolor image obtained with the first set of

transformation functions: explosive and background have different intensity levels and are mapped to different and are mapped to different colors. The block, however, is quite uniform.

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Intensity to color transformation

Trimmed sinusoidal functions used for the intensity transformations in the previous example.

Changing the phase and frequency of each sinusoid can emphasize (in color) ranges in the gray scale: if all three transformations have the same phase and frequency, the output image would be monochrome. A small change in phase

Spring 2008 ELEN 4304/5365 DIP 11

monochrome. A small change in phase between 3 transformations leads to a slight change in pixels, whose intensities correspond to peaks. Pixels with intensity values in the steep section of sinusoids are assigned to much stronger colors.

Intensity to color transformation

Often, it is desired to combine several monochrome images monochrome images into a single color composite image.

A frequent use of this approach is in multispectral image A frequent use of this approach is in multispectral image processing: different sensors produce individual monochrome images, each in a different spectral band. Next, three images can be selected for display (based, for instance, on a type of

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Intensity to color

transformation

Spectral satellite images of DC area: red, green, blue, and near infrared

infrared.

First 3 images combined into a full-color image – sometimes (dense areas) are hard to interpret.

Red component replaced by the

Spring 2008 ELEN 4304/5365 DIP 13

Red component replaced by the near IR (component with strong response to biomass): biomass is represented in red and urban features appear grayish.

Intensity to color transformation

Images of Io combined from different Galileo imagers (some of them are in an invisible region). However, g ) ,

understanding of chemical and physical processes affecting sensor responses helps building meaningful color maps: Material newly ejected from active volcanoes are mapped to red; older sulfur deposits are indicated by yellow sulfur deposits are indicated by yellow. Such composite images might be easier to understand and interpret than

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Full-color image processing basics

There are two major categories of full-color image processing: 1) Process each component image (R,G,B, for instance)

individually and then form a composite processed image; 2) Directly work with color pixels: since full-color images have at

least 3 components, color pixels are vectors… In RGB color space, an arbitrary vector (color pixel) is

( , ) ( , )

R

c x y R x y

⎡ ⎤ ⎡ ⎤

Spring 2008 ELEN 4304/5365 DIP 15

( , ) ( , ) ( , , ) ( , ) ( , ) ( , ) ( , ) R G B c x y R x y x y z c x y G x y c x y B x y ⎡ ⎤ ⎡ ⎤ ⎢ ⎥ ⎢ ⎥ =_⎢ _{⎥ ⎢}= _⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎣ ⎦ c

Whose components are the RGB components of a color image at a point. Note that the vector components are spatial variables!

Full-color image processing basics

It might be easier to process each individual component image but the result of such processing is not always equivalent to direct processing. In order to both processings to be equivalent:

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1) The process

(filtering) has to be applicable to both vectors and scalars; 2) The operation on h t f each component of a vector must be independent of the other components.