Presenetation Image Processing_by Sudipto Saha

8/6/2019 Presenetation Image Processing_by Sudipto Saha

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mageProcessingBasic Concepts Of

Photo Courtesy:TemplatesWise

SUDIPTA SAHADEPT-CSEROLL-56


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INTRODUCTIONINTRODUCTION

In electrical engineering and computer scienceimage processing is any form of signal processingfor which the input is an image, such as aphotograph or video frame; the output of imageprocessing may be either an image or, a set ofcharacteristics or parameters related to the image.Most image-processing techniques involve treatingthe image as a two-dimensional signal andapplying standard signal-processing techniques toit.

Image processing usually refers to digital image

processing, but optical and analog imageprocessing also are possible. This presentation isabout general techniques that apply to digitalimage processing. The acquisition of images(producing the input image in the first place) isreferred to as imaging.


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Image processing can be broken down into several sub-categories, including:

Imagecompression, image enhancement,image filtering, image distortion, image display andcoloring.

Before enhancement , filtering & others we first need to

know how image Is represented in computer.

Images are signals which can be represented by periodicfunctions by Fourier Transform.

1.Image2.Magnitude in FT3.Phase in FT

1 2 3


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After getting these transformations , these are modulated to getThe desired result.You cant even imagine what a fourier analysis

Offers the flexibility to change an image.

A little mathematical analysis.The 2D Fourier Transform of a Digital Image

Let I(r,c) be a single-band digital image with r rows and c

Columns.Then I(r,c) has Fourier representation-

R-1 c-1I(r,c)= f(u,v)e^[i2 (ur/R+vc/C)]

u=0 v=0

WhereR-1 C-1

f(u,v)=1/RC I(r,c)e^[-i2 (ur/R+vc/C)]r=0 c=0

R & C FOURIER CO-EFFICIENTS.


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Anyway , going deep into those mathematics is beyond scope.Moving onto some processing techniques , starting on with

IMAGE FILTERING-

Image FILTERING is useful for noise reduction andedge enhancement.Types of filters used-

MEAN FILTERS(Convolution Filters)-These Filtering methods arebased upon the transformation ofthe image into its scale or spatial frequency components usingthe Fourier transform. The spatial domain filters or theconvolution filters are generally classed as either high-pass(sharpening) or as low-pass (smoothing) filters.

GAUSSIAN FILTERSThey weigh pixels based on their distancefrom the center of the convolution filter. Inparticular,this does a decent job of blurring noise whilepreserving features of the image.


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Effect of mean filters

Effect of Gaussianfilters


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Image Enhancements-----------Image Enhancement techniques are instigated for making satelliteimageries more informative and helping to achieve the goal of image

interpretation. The term enhancement is used to mean the alterationof the appearance of an image in such a way that the informationcontained in that image is more readily interpreted visually in termsof a particular need. The image enhancement techniques are appliedeither to single-band images or separately to the individual bands of amultiband image set.

Density SlicingDensity Slicing is the mapping of a range of contiguous grey levelsof a single band image to a point in the RGB color cube.

Linear Contrast StretchThis technique involves the translation of the image pixel values fromthe observed range to the full range of the displaydevice(generally 0-255, which is the range of values representable inan 8bit display devices.


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Contrast Streching


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Histogram Equalisation

he underlying principle of histograqualisation is straightforward and

,imple it is assumed that each levehe displayed image should contain

n approximately equal number of,ixel values so that the histogram

hese displayed values is almost.niform The objective of theistogram equalisation is to

pread the range of pixel valuesresent in the input image over the

.ange of the display device

Image Histograms


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Edge Detection--- One of the most important uses of image processingis edge detection. Edge detection algorithms typically proceed in three

or four steps:Filtering: cut down on noiseEnhancement: amplify the difference betweenedges and non-edgesDetection: use a threshold operationLocalization (optional): estimate geometry ofedges beyond pixels.

Techniques used-SOBEL OPERATOR- A popular gradient magnitude computation is theSobel operator:We can then compute the magnitude of the vector(sx, sy). Sx & sy are matrices.

LAPLACIAN- An equivalent measure of the second derivative in 2Dis the Laplacian:Using the arguments to compute thegradient filters, we can derive a Laplacian filter.


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Original Laplacian (+128)

Original + Laplacian Original - Laplacian

EFFECTOFLAPLACIANOPERATORONIMAGE TODETECT EDGESAND FLUCTUATESHARPNESS.


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Image Distortion----------A thought may come that why distortion is required!!!

Image distortion is applied in image transformation regionsTo produce effects,blurring,etc.All are image distortion relatedTopics where original image is transformed to get desired result.

There are enough topics in image distortions to discuss for a day.Some important topics are..

-Texture Transfer-Blurring

Texture Transfer - Texture transfer involves correspondence map ofimages, which is spatial map of some corresponding quantity over boththe texture source image and a controlling target image. That

quantity could include image intensity, blurred image intensity, localimage orientation angles, or other derived quantities.


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Source

texture

Targetimage

Texture Transfer


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BLURRING-------

Convolution with fourier analysis of the actual function of image

Can be used to blur.For another convolution function GaussianExponential function gives descent results.Blurring is actually edgedistortion.

( ) ( ) ( )xhxfxg =

CONVOLUTION-

( )

= 2

2

21exp

21

xxh h(x) is Gaussian exponentialEqn.f(x) is fourier transform ofImage in frequency domain.


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->Blurring


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Image coloring.

One practically attractive method of Image Coloring impliesperforming three independent transformations on theintensity of any input pixel.The results are fed separatelyInto the red green and blue monitor channels producing aComposite image whose colors are modulated by theTransformation functions.


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LAST TOPIC:IMAGE COMPRESSION.

Image Compressions are of 2 types

Lossless No data is lost during compression.Very Limited compression ratio.

Lossy -Some data is permanently lost during compression.Done using psycho-visual analysis.

JPEG Image Compression Technique(Lossy)

JPEG algorithm uses 4 steps-

Preprocess-Conversion of R,B,C color channels to YCbCr space.

Transformation-Discrette Cosine Transform is done to change the

domain of the image.Quantisation-Elements near to zero are converted to zero.

Encoding-Advanced Huffman coding is done to remove redundancy.

I


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Photo Courtesy-Yoyogi Park, Tokyo,October 1999. Photo by Alan Peters.


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IMPORTANCE AND SIGNIFICANCE OF IMAGE PROCESSING : 1 EXAMPLE

Airport X-ray scanner: ordinary

luggage and one with a block ofsimulated plastic explosives.Pseudocolor image obtainedwith the first set oftransformation functions:explosive and background

have different intensity levelsand are mapped to differentcolors. The block, however,is quite uniform.Explosives and the bag were mapped by similar transformations:The observer can see through the explosives.


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CONCLUSION-

Image processing is a huge topic to explore and new additionsAre done everyday.Image processing is applied to almost everyField like medical science,space exploration,metallurgy,world offashion,cinematography,etc.Research in this field requires extensiveKnowledge in complex mathematics and analysis.

Finally , thank you all for your patience to sit through this.

Ending this slide with the pioneer-Jean Baptiste Joseph Fourier .

i hJ B ti t J h


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Jean Baptiste JosephJean Baptiste JosephFourierFourier

Had idea (1807): Any periodic

function can berewritten as a weightedsum ofSines andCosines of differentfrequencies.

Dont believe it? Neither did Lagrange,

Laplace, Poissonand other big wigs

Not translated into

English until 1878! But its true!

called Fourier Series

Possibly the greatesttool used in Image

Processing

Deserves An Applause.


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Presenetation Image Processing_by Sudipto Saha