Lec08, Image II (Enhancement), v1.06.ppt -...

Multimedia SystemsMultimedia Systems

Image IIImage II

(Image Enhancement)(Image Enhancement)

Course PresentationCourse Presentation

(Image Enhancement)(Image Enhancement)

Mahdi Amiri

March 2014

Sharif University of Technology

Image EnhancementDefinitionDefinition

Image enhancement deals with the improvement of

visual appearance of the scene, to improve the detectability

of objects to be used by either a machine vision system or a

human observer.

Sources of image deterioration

Multimedia Systems, Mahdi Amiri, Image IIPage 1

Sources of image deterioration

Noise

Low Resolution

Quantization levels

Source of noise

Electronic signal fluctuations in detector ( CCD chip )

Note: Enhancement vs. Restoration

Image enhancement is the improvement of digital

image quality (wanted e.g. for visual inspection or

for machine analysis), without knowledge about the

source of degradation. If the source of degradation is

known, one calls the process image restoration.

Here, we are not going to specifically differentiate

these techniques.

Image EnhancementHave seen so farHave seen so far


Gamma CorrectionGamma Correction Histogram EqualizationHistogram Equalization

Image EnhancementImage NoiseImage Noise

The random variation of brightness or color information in images

An undesirable by-product of image capture


‘grainy’ image, noise from a digital camera‘grainy’ image, noise from a digital camera Image with salt and pepper noiseImage with salt and pepper noise

Image FiltersGaussian smoothingGaussian smoothing

Noise fluctuations are rapid, ie, high frequency.

Gaussian filters are a class of smoothing filters where the kernel

values have a 2D Gaussian shape.


2D Gaussian Filter2D Gaussian Filter

1 2 1

2 4 2

1 2 1

1 4 6 4 1

4 16 24 16 4

6 24 36 24 6

4 16 24 16 4

1 4 6 4 1

1/16

3x3 Kernel3x3 Kernel

1/256

5x5 Kernel5x5 Kernel

1D Gaussian Filter1D Gaussian Filter

Sweep The ImageSweep The Image

Image FiltersMean FilterMean Filter

To replace each pixel value in an image with the mean (`average')

value of its neighbors

Kernel: represents the shape and size of the neighborhood

1/9 1/9 1/9


1/9 1/9 1/9

1/9 1/9 1/9

1/9 1/9 1/9

Typ. Mean Filter Typ. Mean Filter

KernelKernel

A halftone printA halftone print Mean filteredMean filtered

Image FiltersMedian FilterMedian Filter

Replacing each entry with the median of neighboring entries

Nonlinear digital filtering technique

Used to remove noise

Pro: under certain conditions, it preserves edges while removing

noise


noise

1D Example: Input: x = [2 80 6 3], Window size: 3Input: x = [2 80 6 3], Window size: 3

The median filtered output signal y:The median filtered output signal y:

y[1] = Median[2 2 80] = 2 (Left padding with 2)y[1] = Median[2 2 80] = 2 (Left padding with 2)

y[2] = Median[2 80 6] = Median[2 6 80] = 6y[2] = Median[2 80 6] = Median[2 6 80] = 6

y[3] = Median[80 6 3] = Median[3 6 80] = 6y[3] = Median[80 6 3] = Median[3 6 80] = 6

y[4] = Median[6 3 3] = Median[3 3 6] = 3 (Right padding with 3)y[4] = Median[6 3 3] = Median[3 3 6] = 3 (Right padding with 3)

i.e. y = [2 6 6 3].i.e. y = [2 6 6 3].

Median Filter

2D Median Filter Example2D Median Filter Example


Input ImageInput Image Mean Filtered ImageMean Filtered Image Median Median FilterdFilterd ImageImage

Box FilteringOriginal unfiltered imageOriginal unfiltered image


0 0 0

0 1 0

0 0 0

KernelKernel

Box FilteringExample Java CodeExample Java Code

public int[] BoxFiltering(

int[] pixels,int width, int height, float[] kernel) {

int[] temp = new int[width*height] ;

float denominator = 0.0f ;

float red, green, blue ;

int ired, igreen, iblue, indexOffset, rgb ;

int[] indices = {

-(width + 1), -width, -(width - 1),

-1, 0, +1

, width – 1, width, width + 1

} ;

[Part A]

red = green = blue = 0.0f ;

indexOffset = (i*width)+j ;

for (int k=0;k<kernel.length;k++) {

rgb = pixels[indexOffset+indices[k]] ;

red += ((rgb & 0xff0000)>>16)*kernel[k] ;

green += ((rgb & 0xff00)>>8)*kernel[k] ;

blue += (rgb & 0xff)*kernel[k] ;

}

ired = (int)(red / denominator) ;

igreen = (int)(green / denominator) ;

SeeSee

http://techhttp://tech--algorithm.com/articles/boxfilteringalgorithm.com/articles/boxfiltering--

interactiveinteractive--applet/applet/


} ;

for (int i=0;i<kernel.length;i++)

denominator += kernel[i] ;

if (denominator==0.0f) denominator = 1.0f ;

for (int i=1;i<height-1;i++) {

for (int j=1;j<width-1;j++) {

[Include Part A]

}

}

return temp ; }

igreen = (int)(green / denominator) ;

iblue = (int)(blue / denominator) ;

if (ired>0xff) ired = 0xff ;

else if (ired<0) ired = 0 ;

if (igreen>0xff) igreen = 0xff ;

else if (igreen<0) igreen = 0 ;

if (iblue>0xff) iblue = 0xff ;

else if (iblue<0) iblue = 0 ;

temp[indexOffset] = 0xff000000 | ((ired<<16) &

0xff0000) |

((igreen<<8) & 0xff00) | (iblue & 0xff) ;

The parameter pixels is an array containing a total of width*height pixel information. The parameter kernel is an array with The parameter pixels is an array containing a total of width*height pixel information. The parameter kernel is an array with a fa fixed ixed

length of nine. This is because the implementation assumes a window of size 3x3. The function also assume each pixel is in thlength of nine. This is because the implementation assumes a window of size 3x3. The function also assume each pixel is in the e

form ARGB (alpha, red, green, blue), where blue is the least significant byte. Alpha is the transparency information and shouform ARGB (alpha, red, green, blue), where blue is the least significant byte. Alpha is the transparency information and should ld just just

be left untouched. The array indices is a simple optimization table used to find neighboring pixels. The denominator is the sbe left untouched. The array indices is a simple optimization table used to find neighboring pixels. The denominator is the sum um of of

parameter kernel, it will be the denominator when calculating average.parameter kernel, it will be the denominator when calculating average.

Box FilteringSmoothing (~ Mean Filter)Smoothing (~ Mean Filter)

1 1 1

1 2 1

1 1 1

KernelKernel(without denominator)(without denominator)

1/10 1/10 1/10

1/10 2/10 1/10

1/10 1/10 1/10

KernelKernel(with denominator)(with denominator)


0 0 0

0 1 0

0 0 0

1 1 11/10 1/10 1/10

Box FilteringSharpening (Edge Enhancement)Sharpening (Edge Enhancement)

-1 -1 -1

-1 9 -1

-1 -1 -1



-1 -1 -1

0 0 0

0 1 0

0 0 0

Box FilteringRaisedRaised

0 0 -2

0 2 0

1 0 0



0 0 0

0 1 0

0 0 0

1 0 0

Box FilteringMotion BlurMotion Blur

0 0 1

0 0 0

1 0 0



0 0 0

0 1 0

0 0 0

1 0 0

Box FilteringEdge DetectionEdge Detection


-1 -1 -1

-1 8 -1

-1 -1 -1


0 0 0

0 1 0

0 0 0

-1 -1 -1

Image EnhancementDespeckleDespeckle

Speckle detection and deletion.

Allows the removal of speckle in scanned or faxed

images. The speckle is the presence of black points of noise

in images acquired by a scanner or received by fax.


Image EnhancementMorphological OperationsMorphological Operations

Mathematical morphology is a set-theoretical approach to multi-

dimensional digital signal or image analysis, based on shape. The signals

are locally compared with so-called structuring elements S of arbitrary

shape with a reference point R.

We will define these operations without any mathematical rigour, For a

binary image:

Erosion: The eroded image of an object O with respect to a structuring

element S with a reference point R, O��S , is the set of all reference points

for which S is completely contained in O.

structuring

element

Ref.: ikpe1101.ikp.kfa-juelich.de/briefbook_data_analysis/node178.html


for which S is completely contained in O.

Dilation: The dilated image of an object O with respect to a structuring

element S with a reference point R, O��S , is the set of all reference points

for which O and S have at least one common point.

Opening: Is defined as an erosion, followed by a dilation: (O��S)��S

Closing: Is defined as a dilation, followed by an erosion: (O��S)��S

Sample structuring elements

Morphological OperationsA Simple ExampleA Simple Example

Structural

element

(SE)


Erosion with Z8 Dilation with Z8

Other example application: Major preprocessing step for

object tracking or optical character recognition.

Morphological OperationsExample ApplicationExample Application

original frame

Color filtering based object tracking application example


HSV thresholded image

H: 32-256, S: 142-196, V: 0-256After erosion and dilation (opening)

Erosion SE: 3×3 Rect. (Z8)

Dilation SE: 8×8 Rect. (Z8)

Now the desired object can be detected by

finding the largest closed area.

original frame

(zoomed-out)

640×480

Color AdjustmentsExample of color correction in PhotoshopExample of color correction in Photoshop


White balancing

method for color

correction is shown in

the next slid.

White BalancingColor TemperatureColor Temperature

Note: 25°C (Celsius )

(about 77° Fahrenheit or 298 Kelvin)


Color Temperature comparison Color Temperature comparison

of common electric lamps of common electric lamps

(Warmer light ~ Lower color (Warmer light ~ Lower color

temperature)temperature)

Color Temperature: Based on the

color given off by a glowing hot

piece of platinum.

When moving from a bright daylight environment to a room lit by a candle, our When moving from a bright daylight environment to a room lit by a candle, our

brain can quickly adjust to the changes, making white appear white, whereas brain can quickly adjust to the changes, making white appear white, whereas

film is balanced for one particular color and anything that deviates from this will film is balanced for one particular color and anything that deviates from this will

produce a color cast.produce a color cast.

Incorrect white balance Correct white balance

Using portable references for white balancing

Example for the psychological effects of color: A warmer (i.e., lower color temperature) Example for the psychological effects of color: A warmer (i.e., lower color temperature)

light is often used in public areas to promote relaxation, while a cooler (higher color light is often used in public areas to promote relaxation, while a cooler (higher color

temperature) light is used to enhance concentration in offices.temperature) light is used to enhance concentration in offices.

Color TemperatureThe Psychology of ColorThe Psychology of Color

While perceptions of color are somewhat

subjective, there are some color effects

that have universal meaning.


Source: www.100wpthemes.com/building-website/wordpress-tips

/how-to-choose-a-color-scheme-for-your-wordpress-theme/

Office: Blue, Most productive color.

Bedroom: Green, Tranquility and Health.

Girl's Room: Pink, Calming, Warm.

Kitchen: Yellow, Increases metabolism, brightens room,

gives you energy.

Living Room: Lavender (pale purple), Calms the nerves,

allows relaxation.

Dining Room: Red, Encourages appetite.

The Psychology of ColorThe Psychology of Color


Image EnhancementRealReal--time Filteringtime Filtering

Application

HD video conferencing, multi-focus imaging


Image EditorsPaint.NETPaint.NET

Paint.NET is a proprietary freeware raster graphics editor program

for Microsoft Windows, developed on the .NET Framework.

http://www.getpaint.net/http://www.getpaint.net/


Image EditorsGIMPGIMP

GIMP (short for the GNU Image Manipulation Program) is a free software

raster graphics editor. (Microsoft's Windows, Apple's Mac OS X, GNU/Linux)

http://www.gimp.org/http://www.gimp.org/


SeeSee: http://en.wikipedia.org/wiki/Comparison_of_raster_graphics_editors: http://en.wikipedia.org/wiki/Comparison_of_raster_graphics_editors

GIMP 2.2.8 running under X11 on Mac OS XGIMP 2.2.8 running under X11 on Mac OS X GIMP 2.6 running on GIMP 2.6 running on UbuntuUbuntu

Thank You

Multimedia SystemsMultimedia Systems

Image IIImage II


Thank You

1. http://ce.sharif.edu/~m_amiri/

2. http://www.dml.ir/

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