Image Processing Homework: Blurring Function Transfer Function and Convolution - Prof. Tho, Assignments of Digital Signal Processing

The solutions to problem 1 and problem 2 from the ece 6364 spring 2009 homework 11. Problem 1 deals with finding the transfer function of a blurring function using fresnel integrals, while problem 2 involves computing the linear convolution of two 1-d images using discrete fourier transforms (dfts).

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ECE 6364 Spring 2009 HW 11 Due 4/27
Problem 1.
Given an image blurred by camera motion
[]
)()(
2
)( 2
0Ttutu
ta
tx =
0)(
0=ty
Find the transfer function ),( vuH for the blurring function.
Hint:
=xdxC
0
2)cos()(
γγ
is known as the Fresnel Cosine integral and
=xdxS
0
2)sin()(
γγ
as the Fresnel Sine integral.
There is no known closed form solution.
)(xC and )(xS must be evaluated via numerical integration. )(xC and )(xS versus x are available in table form.
When you use )(xC or )(xS in a program (e.g. image restoration in the presence of motion blur) and you want the program to work for
different values of x, you simply load the table into a matrix and index the entry that applies from within the program.
In deriving a problem solution, one simply replaces
xd
0
2)cos(
γγ
with )(xC and continues towards the answer, knowing that )(xC is
a well-defined quantity, known to all.
Problem 2.
You want to compute the linear convolution of two 1-D images ][**][ fedcba using DFTs. You load the two 1-D images into
zero-padded vectors as
[]
cba ][00 and
[]
0][0 fed , compute the DFTs of the two vectors, multiply the two DFTs
together term-by-term, and then take the inverse DFT. Your result is the vector
[
]
9.52.40.32.21.1 . Determine the numerical
result of the linear convolution ][**][ fedcba , marking the 0th pixel with a box.
Problem 3.
Let
=
987
6]5[4
321
H be a blurring mask, and
=dc
ba][
X be a 2x2 image. Where
=
tsrq
ponm
lkji
hgfe
][
** XH , we are only
interested in the central 2x2 portion
on
kj][ of the linear convolution that is the same size as the image X.
(go to next page)
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ECE 6364 Spring 2009 HW 11 Due 4/

Problem 1.

Given an image blurred by camera motion

[ () ( )]

2

()

2

0 ut ut T

at x t = − −

y 0 ( t )= 0

Find the transfer function H ( u , v )for the blurring function.

Hint:

x C x d 0

2 ( ) cos(γ ) γ is known as the Fresnel Cosine integral and = (^) ∫

x S x d 0

2 ( ) sin(γ ) γ as the Fresnel Sine integral.

There is no known closed form solution.

C ( x )and S ( x )must be evaluated via numerical integration. C ( x )and S ( x )versus x are available in table form.

When you use C ( x )or S ( x )in a program (e.g. image restoration in the presence of motion blur) and you want the program to work for

different values of x , you simply load the table into a matrix and index the entry that applies from within the program.

In deriving a problem solution, one simply replaces (^) ∫

x d 0

2 cos(γ ) γ with C ( x )and continues towards the answer, knowing that C ( x )is

a well-defined quantity, known to all.

Problem 2.

You want to compute the linear convolution of two 1-D images a [ b ] c ** de [ f ]using DFTs. You load the two 1-D images into

zero-padded vectors as [ 0 0 a [ b ] c ]and [ 0 d e [ f ] 0 ], compute the DFTs of the two vectors, multiply the two DFTs

together term-by-term, and then take the inverse DFT. Your result is the vector [ 1. 1 2. 2 3. 0 4. 2 5. 9 ]. Determine the numerical

result of the linear convolution a [ b ] c ** de [ f ], marking the 0th pixel with a box.

Problem 3.

Let

4 [ 5 ] 6

H be a blurring mask, and (^) ⎥ ⎦

c d

[ a ] b X be a 2x2 image. Where

q r s t

m n o p

i j k l

e f g h

[ ] H ** X , we are only

interested in the central 2x2 portion (^) ⎥

n o

[ j ] k of the linear convolution that is the same size as the image X.

(go to next page)

Consider the row-ordered vector

d

c

b

[ a ]

x

r from X , and the linear system

y B x

r r = where B is 4x4, y

r is a 4x1 row-ordered vector

o

n

k

[ j ]

that is the row-ordered central 2x2 portion (^) ⎥ ⎦

n o

[ j ] k of the linear

convolution.

(a) Fill-in the elements of B using the elements of H

(b) Form the transpose matrix

t B and consider the operation z B x

r (^) t r = that is also equivalent to a convolution operation. What is the

equivalent convolving mask?