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EEN330: Random Signals & Noise

Lab Assignment 3

Image Processing

Prepared by: Eng. Rima Masri Delivered by: Eng. Samr Samir Ali

May 15, 2016

This lab assignment aims to consolidate the students concepts about digital image noise and filtering techniques.

Instructions:

• Follow the given instructions and hints while solving your experiment.

• Use the help xyz command to get more information on command xyz().

• Answer the questions given in the lab manual and provide the Matlab codes and snapshots of the outputs.

Lab Experiment (i) Basic MATLAB Commands Regarding Image Viewing

In the first part of our lab, we are going to write a Matlab code to view gray, black, and white colors separately.

(a) Open Matlab and create a new script.

(b) Create a 256 x 256 matrix with a value equal to 128. Each cell in the matrix equals to 128.

(c) Cast the matrix to ’uint8’ and display it.

(d) What do you observe? What color does 128 represent?

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(e) Change the values of the matrix in part (b) to get a black color.

(f) Write a code that gradually view black colors to pure white.[Hint: use for loop and pause command].

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(ii) Types of Noise

In the second part of our lab, we are going to expose an image to two different types of noises: Gaussian noise and Salt & Pepper noise.

(ii)(a) Gaussian Noise (a) Save ’cameraman.tif’ image in a variable called I and display the image.

(b) Experiment on Gaussian noise with mean 0.5 and compare the effect of variance on the noise image by increasing its value.

• initialize the mean ’m’ to 0.5 and the variance ’v’ to 0.000001

• Update the values of ’I’ by coverting it to double using im2double() function.

• Use a for loop to iterate the following operation 6 times: 1 Create a Gaussian noise in image ’I’ using ’m’ and ’v’ values.

2 Display the noise in black (not grayscale)

3 Update ’v’ to be equal to v*10 (repeat the same process with new value of ’v’)

(c) Save and run the script. What do you observe?

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(ii)(b) Salt & Pepper Noise (a) Add the Salt & Pepper noise to the image’I’

(b) Display the noise image.

(c) Run the script, what do you observe?

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(iii) Types of Image filtering

(iii)(a) Median filtering (a) Filter the image with salt & pepper noise and display the result.[Hint:use the command medfilt2()].

(b) Filter the image with Gaussian noise (mean=0, variance=0.01) and display the result.

(c) From the pervious observtions. Median filter does better job for removing ———– noise.

(iii)(b) Low Pass filtering (a) Create a low pass filter ’h’ with size 3x3. The value of each cell in the filter equals to 1/(3*3).

(b) Convolve the image that has the Gaussian noise (type: double) with the filter ’h’ using conv().

(c) Display the convolution result after casting it to ’uint8’

(d) increase the size of the filter window by creating a filter with size 25x25. Each cell in the matrix is equal to 1/(25*25).

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(e) Repeat the steps in (b) and (c) and compare the results with the ones in (c).

(iii)(c) Wiener filtering (a) Do Wiener filtering on 3x3 block of the salt & pepper noisy image and display the result. [Hint:use

the command wiener2()].

(b) Increase the block size to 7x7 and apply Wiener again.

(c) Next, keep the size as 7 and provide the function with information about noise=0.01 and compare the images.

(d) Finally, provide the Wiener filtering with decreased noise parameter = 0.001 on the Gaussian noisy image with mean = 0 and variance = 0.001. Compare between the noisy image and the filterd one.