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ECE 5256
Matlab project: Read and display an image and Affine transformation
1 Determine a process where you can acquire an image and use Matlab commands to get an
image into Matlab. Use the command imread.
2 Read in an image and display it. An image can be displayed using the command image(a),
where a is the variable that contains the image. The image command usually assigns 64 intensity
levels between 0 and 63. The colormap command allows various look up tables to be used. To
display an image in grayscale, use the command colormap(gray). Values greater than 63 are
mapped to white, and below 0 are mapped to black. This can be inconvenient so
the imagesc command may be used. This command automatically scales the display. The largest
value of the image is set to white, and the lowest is set to black with a total of 64 intensity levels.
In addition, you can set the scaling to whatever you want. The example below scales an image
between 0 and x. Anything less than x gets mapped to black, and anything greater than x gets
mapped to white.
clims=[0,x];
imagesc(image, clims)
Note that the value of the variable doesn’t change in any of these examples, only the display on
the screen.
Make yourself familiar with axis commands such as: “image,” “off,” etc. to make the display of
the image look better.
3 Read in an image and scale the intensity values from 0 – 255. Set the maximum value to 255,
and the minimum to 0.
Add noise to the image with a mean of 0, and standard deviation of 20 using the randn command.
Take the average of N noisy versions of the original (using independent noise samples (randn
commands)!) for values of N = 1, 5, and 10 and compare the result to the original image using
the mean-squared error (MSE). Does the MSE decrease with N or the sqrt(N)?
The MSE is a scalar and is calculated as (original image – averaged image)2
4 Affine transformation. Read in an image, and rotate and scale it by 35 degrees and a scale of
0.7 in all directions.
Turn in the following:
1 Cover page indicating: name, course, project number.
2 A brief explanation of what you did.
3 Image used in part 3
4 MSE vs. N for noisy images.
5 Result of affine transformation.
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