ECE-S490 Digital Image Processing Homework 2, Exercises of Digital Signal Processing

Three image processing problems related to 2d-fir filters, location estimates, and the laplacian operator. Students are expected to use the given information to answer questions about the average brightness of filtered images, the properties of median filters, and the identification of the discrete approximation for a laplace mask.

Typology: Exercises

2012/2013

Uploaded on 05/18/2013

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ECE-S490 Digital Image Processing
Homework 2
1. Let h(m,n), m=1,..,M, n=1,..,N be a 2D-FIR filter kernel.
Let
=
NM,
nm,
1n)h(m,
,
Let X and Y be images and Y=h*X
Show that the average brightness of Y is equal to the average brightness of X.
2. Let
|
ws s
Xθ|Argminθ
ˆ
=
be a location estimate of {Xs, s w}. Show
that
θ
ˆ
is the median of {Xs, s w}.
3. Is median filter homogeneous?
Is median filter linear?
Explain.
4. There are different discrete approximations for the Laplacian operator.
One form of discrete approximation and the resulting Laplace mask for this
approximation is given in class as:
0210
21221
0210
/
//
/
Determine the underlying discrete approximation for the Laplace mask given
below:
111
181
111
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ECE-S490 Digital Image Processing

Homework 2

  1. Let h(m,n), m=1,..,M, n=1,..,N be a 2D-FIR filter kernel.

Let ∑ =

M, N

m,n

h(m,n) 1 ,

Let X and Y be images and Y=h*X

Show that the average brightness of Y is equal to the average brightness of X.

  1. Let (^) | s w

θ Argmin |θ Xs

= − be a location estimate of {Xs, s∈^ w}. Show

that θˆ^ is the median of {Xs, s∈ w}.

  1. Is median filter homogeneous? Is median filter linear? Explain.
  2. There are different discrete approximations for the Laplacian operator. One form of discrete approximation and the resulting Laplace mask for this approximation is given in class as:

Determine the underlying discrete approximation for the Laplace mask given below:

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