Image resizing and cropping in MATLAB - Deep Dive

Overview - Image resizing and cropping

What is it?

Image resizing changes the size of an image by increasing or decreasing its width and height. Cropping cuts out a specific part of an image, removing unwanted areas. Both techniques help focus on important details or fit images into specific spaces. They are basic tools in image processing used in many applications.

Why it matters

Without resizing and cropping, images might not fit screens or analysis tools properly, causing poor display or inaccurate results. For example, a photo too large for a website slows loading, and irrelevant parts in an image can confuse analysis. These techniques make images easier to use, faster to process, and clearer to understand.

Where it fits

Before learning image resizing and cropping, you should understand basic image representation like pixels and color channels. After mastering these, you can explore advanced image processing like filtering, segmentation, and feature extraction.

Mental Model

Core Idea

Image resizing changes the image size by adjusting pixels, while cropping cuts out a chosen rectangular area to focus on important parts.

Think of it like...

Imagine a photo printed on paper: resizing is like shrinking or enlarging the photo to fit a frame, and cropping is like cutting out a smaller piece of the photo to keep only what you want.

Original Image
┌─────────────────────┐
│                     │
│      [Picture]       │
│                     │
└─────────────────────┘

Resizing (smaller or bigger)
┌───────────┐
│ [Smaller] │
└───────────┘

Cropping (cut a part)
┌───────────────┐
│   [Cropped]   │
│   [Section]   │
└───────────────┘

Build-Up - 7 Steps

1

Foundation

Understanding Image Pixels and Size

Concept: Images are made of pixels arranged in rows and columns, each pixel holding color information.

An image in MATLAB is a matrix where each element represents a pixel's color or brightness. The size of this matrix (rows x columns) defines the image's dimensions. For example, a 100x100 matrix means the image is 100 pixels tall and 100 pixels wide.

Result

You can see the size of an image matrix using MATLAB's size() function, which returns the number of rows and columns.

Understanding that images are just matrices helps you realize resizing and cropping are about changing or selecting parts of these matrices.

2

Foundation

Basic Image Display and Size Checking

Concept: You can view images and check their size in MATLAB to understand their dimensions before processing.

Use imread() to load an image, imshow() to display it, and size() to check its dimensions. For example: img = imread('peppers.png'); imshow(img); imgSize = size(img);

Result

The image appears on screen, and imgSize shows its height, width, and color channels.

Seeing the image and its size helps you connect the matrix data to the visual picture, preparing you for resizing and cropping.

3

Intermediate

Resizing Images with imresize

🤔Before reading on: do you think resizing changes the image quality or just the size? Commit to your answer.

Concept: MATLAB's imresize function changes image dimensions by adding or removing pixels, using interpolation to keep the image clear.

To resize an image, use imresize(img, scale) where scale is a number like 0.5 to shrink or 2 to enlarge. For example: smallImg = imresize(img, 0.5); imshow(smallImg);

Result

The displayed image is smaller or larger depending on the scale, but still recognizable.

Knowing that resizing uses interpolation explains why images can stay clear even when their size changes.

4

Intermediate

Cropping Images with imcrop

🤔Before reading on: do you think cropping changes the image size or just cuts part out? Commit to your answer.

Concept: imcrop lets you select a rectangular area of the image to keep, removing everything outside that area.

Use imcrop(img, rect) where rect = [x y width height] defines the crop area. For example: croppedImg = imcrop(img, [50 50 100 100]); imshow(croppedImg);

Result

Only the selected part of the image is shown, smaller than the original.

Understanding cropping as selecting a submatrix helps you focus on important image parts for analysis or display.

5

Intermediate

Combining Resizing and Cropping

🤔Before reading on: do you think resizing before cropping or cropping before resizing changes the final image? Commit to your answer.

Concept: The order of resizing and cropping affects the final image size and quality.

Try resizing first then cropping: resizedImg = imresize(img, 0.5); croppedAfterResize = imcrop(resizedImg, [10 10 50 50]); imshow(croppedAfterResize); Or cropping first then resizing: croppedBeforeResize = imcrop(img, [50 50 100 100]); resizedAfterCrop = imresize(croppedBeforeResize, 0.5); imshow(resizedAfterCrop);

Result

The two images differ in size and detail depending on the order.

Knowing the order matters helps you control image quality and focus for your specific needs.

6

Advanced

Interpolation Methods in Resizing

🤔Before reading on: do you think all interpolation methods produce the same resized image? Commit to your answer.

Concept: imresize supports different interpolation methods like 'nearest', 'bilinear', and 'bicubic' affecting image smoothness and sharpness.

Example: imgNearest = imresize(img, 0.5, 'nearest'); imgBilinear = imresize(img, 0.5, 'bilinear'); imgBicubic = imresize(img, 0.5, 'bicubic'); imshowpair(imgNearest, imgBicubic, 'montage');

Result

Images show different smoothness and edge quality depending on interpolation.

Understanding interpolation choices lets you balance speed and image quality for your application.

7

Expert

Handling Aspect Ratio and Padding

🤔Before reading on: do you think resizing always keeps the original image shape? Commit to your answer.

Concept: Resizing can distort images if aspect ratio is not preserved; padding can add borders to keep shape without distortion.

To keep aspect ratio: scale = min(targetWidth/width, targetHeight/height); resizedImg = imresize(img, scale); To pad: paddedImg = padarray(resizedImg, [padHeight padWidth], backgroundColor, 'both');

Result

The image fits target size without stretching, with added borders if needed.

Knowing how to preserve aspect ratio and use padding prevents unwanted image distortion in professional work.

Under the Hood

Resizing changes the image matrix size by calculating new pixel values through interpolation, which estimates colors between existing pixels. Cropping extracts a submatrix by selecting rows and columns within specified coordinates. MATLAB handles color images as 3D arrays, resizing and cropping each color channel consistently.

Why designed this way?

Interpolation methods balance speed and quality, allowing flexible resizing. Cropping as submatrix selection is simple and efficient. These designs fit many image types and use cases, making MATLAB functions versatile and easy to use.

Original Image Matrix
┌─────────────────────────────┐
│ Pixels arranged in rows and │
│ columns with color channels  │
└─────────────────────────────┘

Resizing Process
┌───────────────┐     ┌───────────────┐
│ Original Size │ --> │ New Size      │
│ (M x N x 3)   │     │ (P x Q x 3)   │
└───────────────┘     └───────────────┘
       │                     ▲
       │ Interpolation       │
       └─────────────────────┘

Cropping Process
┌─────────────────────────────┐
│ Select rows and columns      │
│ within rectangle [x y w h]   │
└─────────────────────────────┘
       ↓
┌─────────────────────────────┐
│ Cropped Image Matrix         │
│ Smaller M' x N' x 3          │
└─────────────────────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does resizing always keep the image quality perfect? Commit to yes or no.

Common Belief: Resizing an image just changes its size without affecting quality.

Reality: Resizing can reduce quality due to pixel interpolation, causing blurriness or artifacts, especially when enlarging.

Why it matters: Ignoring quality loss can lead to poor visual results or inaccurate analysis in applications like medical imaging.

Quick: Does cropping change the original image data outside the crop area? Commit to yes or no.

Common Belief: Cropping deletes the unwanted parts from the original image permanently.

Reality: Cropping creates a new image with selected parts; the original image remains unchanged unless overwritten.

Why it matters: Misunderstanding this can cause accidental data loss if the original image is overwritten without backup.

Quick: Is the order of resizing and cropping irrelevant? Commit to yes or no.

Common Belief: You can resize and crop in any order and get the same result.

Reality: The order affects the final image size and detail; resizing first then cropping differs from cropping first then resizing.

Why it matters: Wrong order can cause unexpected image distortion or loss of important details.

Quick: Does MATLAB's imresize only use one interpolation method? Commit to yes or no.

Common Belief: imresize uses a fixed interpolation method that cannot be changed.

Reality: imresize supports multiple interpolation methods like 'nearest', 'bilinear', and 'bicubic' selectable by the user.

Why it matters: Not knowing this limits control over image quality and processing speed.

Expert Zone

1

Resizing color images requires consistent interpolation across all color channels to avoid color shifts.

2

Cropping coordinates are zero-based in MATLAB, so off-by-one errors can crop unexpected areas.

3

Interpolation methods affect not only visual quality but also downstream tasks like edge detection or feature extraction.

When NOT to use

Avoid resizing when exact pixel data is critical, such as in medical imaging diagnostics; use cropping carefully when original context is needed. Alternatives include padding or using multi-resolution analysis instead of resizing.

Production Patterns

In real-world systems, images are often resized to standard sizes for machine learning models, then cropped to focus on regions of interest. Automated pipelines use batch resizing and cropping with metadata to track transformations.

Connections

Matrix slicing

Cropping is a direct application of matrix slicing in programming.

Understanding matrix slicing helps grasp how cropping selects image parts efficiently.

Interpolation in numerical analysis

Image resizing uses interpolation methods similar to those in numerical data estimation.

Knowing interpolation in math clarifies how new pixel values are estimated during resizing.

Graphic design layout

Resizing and cropping images are fundamental in arranging visual elements in design.

Recognizing this connection shows how technical image processing supports creative visual communication.

Common Pitfalls

#1 Resizing without preserving aspect ratio causes image distortion.

Wrong approach:

resizedImg = imresize(img, [100 200]); % Forces height=100, width=200 without aspect ratio

Correct approach:

scale = 100 / size(img, 1); % Calculate scale to keep aspect ratio
resizedImg = imresize(img, scale);

Root cause:

Not understanding that specifying both dimensions directly can stretch or squash the image.

#2 Cropping with incorrect rectangle coordinates crops wrong area or errors.

Wrong approach:

croppedImg = imcrop(img, [width height x y]); % Wrong order of rectangle parameters

Correct approach:

croppedImg = imcrop(img, [x y width height]); % Correct rectangle format

Root cause:

Confusing the order of rectangle parameters in imcrop function.

#3 Overwriting original image variable after cropping without backup.

Wrong approach:

img = imcrop(img, [50 50 100 100]); % Original image lost

Correct approach:

croppedImg = imcrop(img, [50 50 100 100]); % Keep original intact

Root cause:

Not preserving original data before destructive operations.

Key Takeaways

Images are matrices of pixels, and resizing changes their dimensions by interpolating pixel values.

Cropping selects a rectangular part of an image by slicing the matrix, focusing on important areas.

Interpolation methods affect the quality of resized images and should be chosen based on the use case.

The order of resizing and cropping matters and changes the final image's appearance and size.

Preserving aspect ratio during resizing prevents distortion, and padding can help fit images into fixed sizes.

Practice

1. What does the imresize function do in MATLAB when working with images?

easy

A. Converts an image to grayscale

B. Extracts a rectangular part of an image

C. Changes the size of an image by scaling or specifying new dimensions

D. Rotates an image by a specified angle

Solution

1. Step 1: Understand the purpose of imresize

   The imresize function is used to change the size of an image either by scaling it up or down or by specifying exact new dimensions.

2. Step 2: Differentiate from cropping

   Unlike cropping, which extracts a part of the image, resizing changes the entire image size.

3. Final Answer:

   Changes the size of an image by scaling or specifying new dimensions -> Option C

4. Quick Check:

   imresize = Resize image size [OK]

Hint: Resize changes whole image size, crop extracts part [OK]

Common Mistakes:

• Confusing resize with crop
• Thinking resize changes image colors
• Assuming resize rotates image

2. Which of the following is the correct syntax to crop an image img to a rectangle starting at (50, 30) with width 100 and height 80 in MATLAB?

easy

A. cropped = imcrop(img, [50 30 100 80]);

B. cropped = imresize(img, [50 30 100 80]);

C. cropped = crop(img, 50, 30, 100, 80);

D. cropped = imcrop(img, [30 50 80 100]);

Solution

1. Step 1: Recall imcrop syntax

   The imcrop function takes the image and a rectangle vector [x y width height]. Here, x=50, y=30, width=100, height=80.

2. Step 2: Check option correctness

   cropped = imcrop(img, [50 30 100 80]); matches the correct syntax. cropped = imresize(img, [50 30 100 80]); uses imresize which is wrong. cropped = crop(img, 50, 30, 100, 80); uses a non-existent function. cropped = imcrop(img, [30 50 80 100]); swaps x and y and width and height incorrectly.

3. Final Answer:

   cropped = imcrop(img, [50 30 100 80]); -> Option A

4. Quick Check:

   imcrop(img, [x y width height]) = correct crop syntax [OK]

Hint: imcrop uses [x y width height] rectangle vector [OK]

Common Mistakes:

• Swapping x and y coordinates
• Using imresize instead of imcrop
• Incorrect rectangle vector order

3. What will be the size of the output image resized_img after running the following MATLAB code?

img = imread('peppers.png');
resized_img = imresize(img, 0.5);

medium

A. Half the width and half the height of the original image

B. Double the width and double the height of the original image

C. Same size as the original image

D. Width halved but height unchanged

Solution

1. Step 1: Understand imresize with scale factor

   Using imresize(img, 0.5) scales the image to 50% of its original size in both width and height.

2. Step 2: Determine output size

   The output image will have half the width and half the height of the original image.

3. Final Answer:

   Half the width and half the height of the original image -> Option A

4. Quick Check:

   imresize with 0.5 scales both dimensions by 0.5 [OK]

Hint: Scale 0.5 halves both width and height [OK]

Common Mistakes:

• Thinking only width or height changes
• Assuming scale factor doubles size
• Confusing imresize with imcrop

4. Identify the error in this MATLAB code snippet intended to crop an image img to a 100x100 square starting at (20, 20):

cropped_img = imcrop(img, [20 20 100]);

medium

A. The image variable name is incorrect

B. The rectangle vector is missing the height value

C. imcrop cannot crop square images

D. The starting coordinates should be (100, 100)

Solution

1. Step 1: Check imcrop rectangle vector format

   The rectangle vector must have four values: [x y width height]. Here only three values are given.

2. Step 2: Identify missing parameter

   The height value is missing, causing a syntax error or unexpected behavior.

3. Final Answer:

   The rectangle vector is missing the height value -> Option B

4. Quick Check:

   imcrop needs [x y width height] vector [OK]

Hint: Rectangle vector must have 4 values [OK]

Common Mistakes:

• Providing only 3 values instead of 4
• Confusing coordinates with size
• Assuming imcrop auto-fills missing values

5. You want to resize an image img to exactly 200x150 pixels and then crop a 100x100 square from the center. Which MATLAB code sequence correctly achieves this?

hard

A. cropped = imcrop(img, [75 50 100 100]); resized = imresize(cropped, [200 150]);

B. resized = imresize(img, [200 150]); cropped = imcrop(resized, [50 25 100 100]);

C. cropped = imcrop(img, [50 25 100 100]); resized = imresize(cropped, [200 150]);

D. resized = imresize(img, [150 200]); cropped = imcrop(resized, [50 25 100 100]);

Solution

1. Step 1: Resize image to 200x150 pixels

   Use imresize(img, [150 200]) to set width=200 and height=150.

2. Step 2: Crop 100x100 square from center

   The center of resized image is at (100, 75). To crop 100x100, start at (50, 25) because 50 = 100 - 100/2 and 25 = 75 - 100/2.

3. Step 3: Verify option correctness

   resized = imresize(img, [150 200]); cropped = imcrop(resized, [50 25 100 100]); matches the correct order and coordinates. resized = imresize(img, [200 150]); cropped = imcrop(resized, [50 25 100 100]); swaps width and height. Options C and D crop before resizing, which is not what is asked.

4. Final Answer:

   resized = imresize(img, [150 200]); cropped = imcrop(resized, [50 25 100 100]); -> Option D

5. Quick Check:

   Resize first, then crop center rectangle [OK]

Hint: Resize first, then crop center using half-dimensions [OK]

Common Mistakes:

• Swapping width and height in resize
• Cropping before resizing
• Incorrect crop start coordinates