Image interpolation in SciPy

Introduction

Image interpolation helps us resize or transform images smoothly by estimating new pixel values.

When you want to enlarge a small photo without making it look blocky.

When you need to rotate or shift an image and keep it clear.

When you want to reduce image size but keep important details.

When you apply filters that require changing image resolution.

When combining images of different sizes into one view.

Syntax

SciPy

from scipy.ndimage import zoom

zoom(input, zoom_factor, order=3)

input: The image array you want to resize.

zoom_factor: How much to scale the image (e.g., 2 means double size).

order: Controls interpolation method (0=nearest, 1=linear, 3=cubic by default).

Examples

Doubles the size of the image using cubic interpolation.

SciPy

zoom(image_array, 2)

Reduces the image size by half using linear interpolation.

SciPy

zoom(image_array, 0.5, order=1)

Scales height by 1 (no change) and width by 2 using nearest neighbor interpolation.

SciPy

zoom(image_array, (1, 2), order=0)

Sample Program

This code creates a small image with one white pixel in the center. Then it enlarges the image by 3 times using cubic interpolation. Finally, it shows both images and prints their sizes.

SciPy

import numpy as np
from scipy.ndimage import zoom
import matplotlib.pyplot as plt

# Create a simple 5x5 grayscale image with a white square in the center
image = np.zeros((5, 5))
image[2, 2] = 1

# Enlarge the image by 3 times using cubic interpolation (order=3)
enlarged_image = zoom(image, 3, order=3)

# Show original and enlarged images side by side
fig, axes = plt.subplots(1, 2, figsize=(6, 3))
axes[0].imshow(image, cmap='gray', interpolation='nearest')
axes[0].set_title('Original Image')
axes[0].axis('off')

axes[1].imshow(enlarged_image, cmap='gray', interpolation='nearest')
axes[1].set_title('Enlarged Image')
axes[1].axis('off')

plt.tight_layout()
plt.show()

# Print shapes to confirm resizing
print(f"Original shape: {image.shape}")
print(f"Enlarged shape: {enlarged_image.shape}")

Important Notes

Higher order values give smoother images but take more time to compute.

Nearest neighbor (order=0) is fastest but can look blocky.

Interpolation works on arrays, so color images need to be handled per channel.

Summary

Image interpolation helps resize images smoothly by estimating new pixels.

Use scipy.ndimage.zoom with order to control smoothness.

Try different zoom factors and orders to get the best image quality for your task.

Practice

1. What does image interpolation do when resizing an image using scipy.ndimage.zoom?

easy

A. It deletes pixels randomly to reduce image size.

B. It estimates new pixel values to make the resized image smooth.

C. It converts the image to grayscale automatically.

D. It changes the image format to JPEG.

Solution

1. Step 1: Understand image resizing

   When resizing, new pixels must be created or removed to fit the new size.

2. Step 2: Role of interpolation

   Interpolation estimates these new pixel values to keep the image smooth and avoid blockiness.

3. Final Answer:

   It estimates new pixel values to make the resized image smooth. -> Option B

4. Quick Check:

   Image interpolation = smooth pixel estimation [OK]

Hint: Interpolation fills new pixels smoothly when resizing images [OK]

Common Mistakes:

• Thinking interpolation deletes pixels randomly
• Confusing interpolation with color conversion
• Assuming interpolation changes image file format

2. Which of the following is the correct way to call scipy.ndimage.zoom to double the size of an image array img with linear interpolation?

easy

A. zoom(img, zoom=2, order=1)

B. zoom(img, scale=2, order=1)

C. zoom(img, zoom=2, interpolation='linear')

D. zoom(img, factor=2, order=1)

Solution

1. Step 1: Check parameter names in scipy.ndimage.zoom

   The correct parameter for resizing factor is zoom, not scale or factor.

2. Step 2: Check interpolation order

   Order=1 means linear interpolation, which is correct. The parameter interpolation does not exist.

3. Final Answer:

   zoom(img, zoom=2, order=1) -> Option A

4. Quick Check:

   zoom param + order=1 for linear [OK]

Hint: Use zoom= factor and order= interpolation level [OK]

Common Mistakes:

• Using wrong parameter names like scale or factor
• Using interpolation='linear' which is invalid
• Confusing order values with interpolation strings

3. Given the code below, what is the shape of zoomed_img?

import numpy as np
from scipy.ndimage import zoom
img = np.zeros((10, 10))
zoomed_img = zoom(img, zoom=1.5, order=3)

medium

A. (15, 10)

B. (10, 10)

C. (20, 20)

D. (15, 15)

Solution

1. Step 1: Understand zoom factor effect on shape

   The zoom factor 1.5 multiplies each dimension by 1.5. Original shape is (10, 10).

2. Step 2: Calculate new shape

   10 * 1.5 = 15 for both height and width, so new shape is (15, 15).

3. Final Answer:

   (15, 15) -> Option D

4. Quick Check:

   Shape scaled by 1.5 = (15, 15) [OK]

Hint: Multiply each dimension by zoom factor for new shape [OK]

Common Mistakes:

• Assuming shape stays same after zoom
• Rounding incorrectly to 20 instead of 15
• Mixing up dimensions and zoom factor

4. What is wrong with this code snippet for zooming an image with cubic interpolation?

from scipy.ndimage import zoom
zoomed = zoom(image, zoom=2, order='3')

medium

A. The zoom function does not support cubic interpolation.

B. The zoom parameter must be less than 1.

C. The order parameter should be an integer, not a string.

D. The image variable must be a list, not an array.

Solution

1. Step 1: Check the type of order parameter

   The order parameter expects an integer (0 to 5), not a string.

2. Step 2: Validate other parameters

   Zoom can be any positive number, cubic interpolation is order=3, and image can be an array.

3. Final Answer:

   The order parameter should be an integer, not a string. -> Option C

4. Quick Check:

   order must be int, not str [OK]

Hint: Use integer for order, not string [OK]

Common Mistakes:

• Passing order as string instead of int
• Thinking zoom must be less than 1
• Believing cubic interpolation unsupported
• Confusing image data type requirements

5. You want to resize a grayscale image stored in a 2D NumPy array img to 3 times its size using cubic interpolation. Which code snippet correctly achieves this and returns the resized image?

hard

A. zoomed_img = zoom(img, zoom=(3, 3), order=3)

B. zoomed_img = zoom(img, zoom=3, order='3')

C. zoomed_img = zoom(img, zoom=3, interpolation='cubic')

D. zoomed_img = zoom(img, scale=3, order=3)

Solution

1. Step 1: Understand zoom parameter for 2D arrays

   For 2D arrays, zoom can be a single float or a tuple for each axis. Using a tuple (3, 3) explicitly scales both dimensions by 3.

2. Step 2: Check interpolation order and parameter names

   Order=3 means cubic interpolation. Parameter interpolation and scale are invalid.

3. Step 3: Choose the best practice

   Using a tuple for zoom is clearer and recommended for 2D images.

4. Final Answer:

   zoomed_img = zoom(img, zoom=(3, 3), order=3) -> Option A

5. Quick Check:

   Tuple zoom + order=3 for cubic [OK]

Hint: Use tuple zoom for each axis and order=3 for cubic [OK]

Common Mistakes:

• Using invalid parameter names like scale or interpolation
• Passing zoom as single float without tuple (less explicit)
• Confusing order values with strings