Image filtering in MATLAB - Mini Project: Build & Apply

Image filtering

📖 Scenario: You have a simple grayscale image represented as a matrix. You want to apply a filter to smooth the image by averaging each pixel with its neighbors.

🎯 Goal: Build a MATLAB program that applies a 3x3 averaging filter to a grayscale image matrix and displays the filtered image.

📋 What You'll Learn

Create a 5x5 matrix called image with specific grayscale values

Create a 3x3 averaging filter matrix called filter

Apply the filter to the image using 2D convolution with conv2

Display the filtered image matrix using disp

💡 Why This Matters

🌍 Real World

Image filtering is used in photography and medical imaging to reduce noise and improve image quality.

💼 Career

Understanding image filtering is important for jobs in computer vision, image processing, and machine learning.

1

Create the image matrix

Create a 5x5 matrix called image with these exact values: [10 20 30 40 50; 60 70 80 90 100; 110 120 130 140 150; 160 170 180 190 200; 210 220 230 240 250]

MATLAB

% Your code here to create the image matrix

Hint

Use square brackets and semicolons to create the matrix exactly as shown.

2

Create the averaging filter

Create a 3x3 matrix called filter where each element is 1/9 to represent an averaging filter.

MATLAB

image = [10 20 30 40 50; 60 70 80 90 100; 110 120 130 140 150; 160 170 180 190 200; 210 220 230 240 250];
% Your code here to create the filter matrix

Hint

Use ones(3,3) to create a 3x3 matrix of ones, then divide by 9.

3

Apply the filter to the image

Use conv2 with image and filter and the option 'same' to create a new matrix called filtered_image.

MATLAB

image = [10 20 30 40 50; 60 70 80 90 100; 110 120 130 140 150; 160 170 180 190 200; 210 220 230 240 250];
filter = ones(3,3) / 9;
% Your code here to apply the filter using conv2

Hint

Use conv2(image, filter, 'same') to keep the output size same as input.

4

Display the filtered image

Use disp to print the matrix filtered_image.

MATLAB

image = [10 20 30 40 50; 60 70 80 90 100; 110 120 130 140 150; 160 170 180 190 200; 210 220 230 240 250];
filter = ones(3,3) / 9;
filtered_image = conv2(image, filter, 'same');
% Your code here to display filtered_image

Hint

Use disp(filtered_image) to show the result matrix.

Practice

1. What is the main purpose of applying an image filter using a kernel in MATLAB?

easy

A. To convert the image to grayscale

B. To modify the image by emphasizing or reducing certain features

C. To increase the image file size

D. To change the image file format

Solution

1. Step 1: Understand what a kernel does

   A kernel is a small matrix used to process an image by changing pixel values based on neighbors.

2. Step 2: Identify the effect of filtering

   Filtering modifies the image to highlight edges, blur, or sharpen features, not file format or size.

3. Final Answer:

   To modify the image by emphasizing or reducing certain features -> Option B

4. Quick Check:

   Image filtering changes features = D [OK]

Hint: Filters change image features, not file type or size [OK]

Common Mistakes:

• Confusing filtering with file format conversion
• Thinking filtering changes image size
• Assuming filtering only converts to grayscale

2. Which of the following is the correct syntax to apply a filter kernel h to an image img using MATLAB's imfilter function?

easy

A. filtered_img = filter(h, img);

B. filtered_img = filter(img, h);

C. filtered_img = imfilter(h, img);

D. filtered_img = imfilter(img, h);

Solution

1. Step 1: Recall MATLAB's imfilter syntax

   The correct syntax is imfilter(image, kernel), where the image is first and kernel second.

2. Step 2: Check each option

   filtered_img = imfilter(img, h); matches the correct syntax. Options B and D use 'filter' which is incorrect. filtered_img = imfilter(h, img); reverses arguments.

3. Final Answer:

   filtered_img = imfilter(img, h); -> Option D

4. Quick Check:

   imfilter(image, kernel) = A [OK]

Hint: imfilter(image, kernel) is the correct order [OK]

Common Mistakes:

• Using 'filter' instead of 'imfilter'
• Swapping image and kernel arguments
• Missing semicolon or parentheses

3. Given the following MATLAB code:

img = [1 2 3; 4 5 6; 7 8 9];
h = [1 0 -1; 1 0 -1; 1 0 -1];
result = imfilter(img, h, 'replicate');

What is the value of result(2,2)?

medium

A. 0

B. 3

C. 6

D. 9

Solution

1. Step 1: Understand the filter and padding

   The kernel h detects vertical edges. 'replicate' pads edges by repeating border values.

2. Step 2: Calculate convolution at (2,2)

   Neighborhood of (2,2):
   1 2 3
   4 5 6
   7 8 9
   Apply kernel:
   (1*1)+(0*2)+(-1*3)+(1*4)+(0*5)+(-1*6)+(1*7)+(0*8)+(-1*9) = 1+0-3+4+0-6+7+0-9 = (1-3)+(4-6)+(7-9) = -2 -2 -2 = -6
   But imfilter flips kernel, so actual sum is 6.

3. Final Answer:

   6 -> Option C

4. Quick Check:

   imfilter result center = 6 [OK]

Hint: Remember imfilter flips kernel before applying [OK]

Common Mistakes:

• Not flipping the kernel before convolution
• Ignoring padding method 'replicate'
• Calculating sum incorrectly

4. Consider this MATLAB code snippet:

img = imread('peppers.png');
h = fspecial('average', 3);
filtered_img = imfilter(img, h, 'same');

What is the error in this code?

medium

A. No error; the code runs correctly

B. imfilter requires grayscale images only

C. The kernel size in fspecial must be odd

D. The 'same' option is invalid for imfilter

Solution

1. Step 1: Check fspecial usage

   fspecial('average', 3) creates a 3x3 averaging filter, which is valid.

2. Step 2: Check imfilter usage

   imfilter supports RGB images and 'same' option returns output same size as input.

3. Final Answer:

   No error; the code runs correctly -> Option A

4. Quick Check:

   fspecial + imfilter with 'same' = A [OK]

Hint: imfilter works with color images and 'same' option [OK]

Common Mistakes:

• Assuming imfilter only works on grayscale
• Thinking 'same' is invalid option
• Believing kernel size must be odd but 3 is odd and valid

5. You want to sharpen an image img in MATLAB by combining the original image with a filtered version using the Laplacian kernel h = fspecial('laplacian', 0.2);. Which code snippet correctly applies this sharpening?

hard

A. sharpened = img - imfilter(img, h, 'replicate');

B. sharpened = img + imfilter(img, h, 'replicate');

C. sharpened = imfilter(img, h, 'replicate') - img;

D. sharpened = imfilter(img, h, 'replicate') + img * 0.2;

Solution

1. Step 1: Understand Laplacian sharpening

   Sharpening = original image minus the Laplacian filtered image to enhance edges.

2. Step 2: Match code to formula

   sharpened = img - imfilter(img, h, 'replicate'); subtracts the filtered image from original, correctly applying sharpening.

3. Final Answer:

   sharpened = img - imfilter(img, h, 'replicate'); -> Option A

4. Quick Check:

   Sharpen = img - Laplacian filtered img = B [OK]

Hint: Sharpen by subtracting Laplacian filtered image [OK]

Common Mistakes:

• Adding instead of subtracting Laplacian result
• Multiplying original image incorrectly
• Confusing Laplacian with averaging filter