Image filtering in MATLAB - Step-by-Step Execution

Concept Flow - Image filtering

Load Image

↓

Define Filter Kernel

↓

Apply Filter (Convolution)

↓

Display Filtered Image

↓

End

The process starts by loading an image, then defining a filter kernel, applying the filter using convolution, and finally displaying the filtered image.

Execution Sample

MATLAB

img = imread('peppers.png');
filter = fspecial('average', [3 3]);
filtered_img = imfilter(img, filter);
imshow(filtered_img);

This code loads an image, applies a 3x3 average filter to blur it, and shows the result.

Execution Table

Step	Action	Variable/Function	Result/Value
1	Load image from file	img = imread('peppers.png')	img is a 3D matrix with image pixel data
2	Create averaging filter kernel	filter = fspecial('average', [3 3])	filter is a 3x3 matrix with all values 1/9
3	Apply filter to image	filtered_img = imfilter(img, filter)	filtered_img is blurred version of img
4	Display filtered image	imshow(filtered_img)	Window shows blurred image
5	End of script	-	-

💡 All steps completed, filtered image displayed

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
img	undefined	Loaded image matrix	Loaded image matrix	Loaded image matrix	Loaded image matrix
filter	undefined	undefined	3x3 averaging kernel	3x3 averaging kernel	3x3 averaging kernel
filtered_img	undefined	undefined	undefined	Blurred image matrix	Blurred image matrix

Key Moments - 3 Insights

Why does the filtered image look blurry compared to the original?

Because the filter used is an averaging filter (see Step 2 and 3 in execution_table), it replaces each pixel with the average of its neighbors, smoothing details and causing blur.

What does the fspecial('average', [3 3]) function create?

It creates a 3x3 matrix where each element is 1/9, which is used to average the pixel values in a 3x3 neighborhood (see Step 2 in execution_table).

Why do we use imfilter instead of just multiplying the image by the filter?

imfilter performs convolution, sliding the filter over the image and combining pixel neighborhoods properly, unlike simple multiplication which doesn't consider neighbors (see Step 3).

Visual Quiz - 3 Questions

Look at the execution_table, what is the value of 'filter' after Step 2?

A A 3x3 matrix with random values

B A 3x3 matrix with all elements equal to 1/9

C The original image matrix

D Undefined

💡 Hint

Check the 'Result/Value' column for Step 2 in execution_table

At which step is the image actually blurred?

A Step 3

B Step 2

C Step 1

D Step 4

💡 Hint

Look at the 'Action' and 'Result/Value' columns in execution_table for Step 3

If we change the filter size from [3 3] to [5 5], how would the variable 'filter' change?

A It becomes a 5x5 matrix with random values

B It stays a 3x3 matrix with elements 1/9

C It becomes a 5x5 matrix with all elements equal to 1/25

D It becomes undefined

💡 Hint

Refer to how fspecial('average', [3 3]) creates the filter in Step 2 and generalize to [5 5]

Concept Snapshot

Image filtering in MATLAB:
- Load image with imread
- Create filter kernel with fspecial
- Apply filter using imfilter (convolution)
- Display result with imshow
Filtering smooths or sharpens images by combining pixel neighborhoods.

Full Transcript

This example shows how MATLAB applies image filtering step-by-step. First, the image is loaded into a matrix. Then, a filter kernel is created using fspecial, here an averaging filter that smooths the image. Next, imfilter applies this kernel to the image by sliding it over every pixel and averaging neighbors, producing a blurred image. Finally, imshow displays the filtered image. Variables img, filter, and filtered_img change as the script runs, showing the image data and filter matrix. Key points include understanding the filter kernel values and why convolution is used instead of simple multiplication. Changing the filter size changes the kernel matrix size and values accordingly.

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