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Morphological image processingImage Restoration
SPEECH AND IMAGE PROCESSING LABMorphological image processing &
Image restoration
Nishil. B. S.Nisha J. S.
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Morphological image processing
What is morphological image processing ?
A broad set of image processing operations thatprocess images based on shapes
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Morphological image processing
Applications of Morphological image processingExtraction of image components
eg. Boundaries, skeletons etc.
Geometric measurementseg. Object location, orientation ,area ,perimeter
Morphological smoothing
To compute the morphological gradient of an image
To compensate uneven background illumination
Granulometry
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Morphological image processing
Value of each pixel in the output image is based on the neighbouringpixels in the input image
Morphological operations require an image and a structuring element
Size and shape of the neighbours depends on the structuring element
Basic morphological operationsErosion
Dilation
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Structuring element
Matrix consisting of only 0’s and 1’s
Can have any arbitrary shape and size
Pixels with values of 1 define the neighborhood
Origin of structuring element identifies the pixel of interest
Matlab syntax
strel(shape, parameters)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Dilation
Grows or thickens objects in a binary image
Manner and extent of thickening controlled by the structuringelement
Rule for dilation
The value of the output pixel is the maximum value of all the pixels inthe input pixel’s neighborhood
Matlab syntax
imdilate(original image, structuring element)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of dilation of binary image
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of dilation of greyscale image
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Erosion
Shrinks or thins objects in a binary image
Manner and extent of shrinking controlled by the structuring element
Rule for erosion
The value of the output pixel is the minimum value of all the pixels in theinput pixel’s neighborhood
Matlab syntax
imerode(original image, structuring element)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of erosion of binary image
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Morphological Opening
Erosion followed by dilation
Smoothens object contours
Breaks thin connections between objects
Removes thin protrusions
matlab syntax
imopen(original image, structuring element)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of opening a binary image
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of opening a binary image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Morphological Closing
Dilation followed by erosion
Joins narrow breaks
Fills long thin gulfs
Fills holes smaller than the structuring element
matlab syntax
imclose(original image, structuring element)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of closing a binary image
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Hit or Miss Transformation
Used to identify specified configurations of pixels
Steps involved in Hit or Miss Transformation
1 Input image is eroded with a structuring element B1
2 Complement of input image is the eroded using a structuringelement B2
3 Logical AND operation is performed on images obtained from step 1and 2
4 Output image consists of 1 in all locations that match the pixels inB1
matlab syntax
bwhitmiss(original image, B1, B2)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of Hit or Miss Transformation of a binary image
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of Hit or Miss Transformation of a binary image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Skeletonization
Skeletonization
Reduce all objects in an image to lines without changing theessential structure of the image
matlab syntax
bwmorph(original image, ‘skel’, inf)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of Skeletonization of a binary image
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Perimeter Determination
Determines the perimeter pixels of the objects in a binary image
A pixel is considered a perimeter pixel if it satisfies both of thesecriteria:
The pixel is onOne (or more) of the pixels in its neighborhood is off
matlab syntax
bwperim(image)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of Perimeterisation of a binary image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Labeling Connected Components
Method to identify objects in a binary image
Pixels in each different object are assigned a unique integer
Type of the chosen connectivity affects the number of objects foundin an image
Definition of object
Set of pixels in a binary image that form a connected group is called anobject or a connected component
matlab syntax
[L num] = bwlabel(image,connectivity)
L = label matrix
num = total number of objects
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Labeling Connected Components
Pixel Connectivity
Connectivity defines which pixels are connected to other pixels
Standard 2D connectivities are :
4-connected8-connected
Custom connectivities can also be specified
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of Labeling of a binary image
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Morpholgical Reconstruction
Repeated dilations of an imageMorphological transformation involving two images and a structuringelementOne image, the marker is the starting point of transformationOther image, the mask constrains the transformation
Morpholgical Reconstruction Algorithm
Initialize h1 to be the marker image
Create the structuring element: B = ones(3)
Repeat:
hk+1 = (hkdilationB) ∩mask
until hk+1 = hk .
matlab syntax
imreconstruct(marker, mask)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Morphological image processingStructuring elementMorhological operations
Illustration of Morpholgical Reconstruction
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Image Restoration
Image RestorationRecovering the desired or perfect image from a degradedversion.
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Image degradation
causes of Image degradation1 degradation due to sensor noise
2 Blur due to camera misfocus
3 Degradation due to camera motion
4 Degradation due to random atmospheric turbulence
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Degradation model
Degradation function together with an additive noise term operates on aninput image to produce degraded image
g(x , y) = h(x , y) ∗ f (x , y) + η(x , y)
f(x,y) Original true imageh(x,y) Degradation function, also called PSF(Point Spread Function)η(x , y) Additive noiseg(x,y) Degraded image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Degradation model cont..
Figure: Degradation model
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Image blurring
A form of bandwidth reduction of the image due to imperfect imageformation process
Caused by relative motion between the camera and the originalscene, or by optical system, which is out of focus
PSF of motion blur is characterized by two parameters namely, blurdirection and blur length
matlab syntax
fspecial(’motion’, blur length, blur angle)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Example of blurred image
Figure: Original and blurred image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Image restoration techniques
1 Inverse filtering
2 Wiener filtering
3 Lucy Richardson Algorithm
4 Blind deconvolution
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Inverse Filtering
This is a very basic restoration filter
Restoration is done using the inverse of degradation function
F̂ (x , y) =G (u, v)
H(u, v)
This filter generally gives poor results
matlab syntax
deconvwnr(degraded image, PSF)
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Wiener Filtering
Used for restoring images in the presence of blur as well as noise
Seeks an estimate f̂ that minimises the statistical error function
e2 = E (f − f̂ )2
Wiener filter function is given by :
F̂ (x , y) = G (u, v)
[|H(u, v)|2
H(u, v) |H(u, v)|2 + Sn(u, v)/Sf (u, v)
]
Sn= noise power spectrumSf = power spectrum of undegraded image
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
wiener filtering
features
This filter gives descent results
It is quite fast
matlab syntax
deconvwnr(degraded image, psf,nspr)nspr = Noise to signal power ratio
deconwnr(degraded image,psf, nacorr, facorr)nacorr = Noise autocorrrelation functionfacorr= Undegraded image autocorrelation function
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Lucy - Richardson Algorithm
Iterative non-linear image restoration technique
Used when only the PSF is known
This algorithm maximises the liklihood function
Features
Reduce the effect of noise amplification
Accounts for nonuniform image quality
Reduces Camera read-out noise
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
matlab syntax
deconvlucy(degraded image, psf,numit, dampar, weight)numit = Number of iterationsdampar = Threshold deviationweight = weight to be assigned to each pixel
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
Blind deconvolution
Image restoration not based on the specific knowledge of PSF
Based on maximum liklihood estimation
Restores the image and the PSF simultaneously using an iterativeprocess similar to the Lucy-Richardson algorithm
matlab syntax
deconvblind(degraded image, intpsf)intpsf = Initial PSF
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration
Morphological image processingImage Restoration
Image degradationDegradation modelImage restoration techniques
References
1 Rafael C. Gonzalez, Richard E. Woods & Steven L. Eddins,“Digitalimage processing using matlab”, Pearson Education Inc.
2 Anil K. Jain, “Fundamentals of digital image processing”,Prentice-Hall Inc.
3 www.prenhall.com/gonzalezwoods
4 www.mathworks.com
Nishil. B. S.Nisha J. S. SPEECH AND IMAGE PROCESSING LAB Morphological image processing & Image restoration