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📌 What is Interpolation? (Simple Explanation)#

Imagine you have a 100-page book, and now you want to shrink it to 50 pages or expand it to 200 pages, but you don't want to lose important information. What would you do?

• Downsampling: You can pick key content and remove the less important parts.
• Upsampling: You can add some extra content between pages to make them read more coherently.

In image processing, interpolation is how to generate new pixels when scaling images, making them look more natural and clearer.

📌 Why is Interpolation Needed?#

Suppose you have a 3×3 pixel image (each square is a pixel), and you want to enlarge it to 6×6, but the question is:
What color should the new pixels be filled with?

• If you directly copy the nearest pixel values, the image will become pixelated (the pixel blocks are very obvious).
• If you calculate the average of surrounding pixels, the image will be smoother but may be a bit blurry.

The interpolation method determines how to fill these new pixels.

In image processing, interpolation is used for:

• Image resizing: When changing the size of an image, interpolation determines the values of new pixels.
• Image rotation: When rotating an image, new pixel values need to be calculated.
• Geometric transformations: Translation, perspective transformations, etc., all require interpolation.

📌 Types of Interpolation#

🔸 1. Nearest Neighbor Interpolation#

🧐 Intuitive Understanding#

“Copy the nearest pixel”
Just like during an exam, if you can't solve a problem, you directly copy the answer from the nearest classmate! 😆

🖼 Example#

You have a 3×3 image:

A  B  C
D  E  F
G  H  I

If you use nearest neighbor interpolation to enlarge it to 6×6, it will directly copy the nearest pixels:

A  A  B  B  C  C
A  A  B  B  C  C
D  D  E  E  F  F
D  D  E  E  F  F
G  G  H  H  I  I
G  G  H  H  I  I

🔹 Characteristics:

• Fast computation, but the image will become pixelated (with obvious pixel blocks).
• Suitable for semantic segmentation masks (because you don't want color mixing).

🔸 2. Bilinear Interpolation#

🧐 Intuitive Understanding#

“Look at the average of four neighbors”
Just like during an exam, you not only copy the nearest classmate's answer but also refer to the answers of four classmates to take the average, making the answer more reliable! 😂

🖼 Example#

Again, a 3×3 image, now enlarged to 6×6:

    A          (A+B)/2        B        (B+C)/2          C  
(A+D)/2      (A+B+D+E)/4    (B+E)/2    (B+C+E+F)/4    (C+F)/2  
    D          (D+E)/2        E          (E+F)/2          F  
(D+G)/2      (D+E+G+H)/4    (E+H)/2    (E+F+H+I)/4    (F+I)/2  
    G          (G+H)/2          H        (H+I)/2        I

🔹 Characteristics:

• Smooth transition, avoiding the pixelation issue of nearest neighbor interpolation.
• Suitable for ordinary image resizing, but details may be a bit blurry.

🔸 3. Bicubic Interpolation#

🧐 Intuitive Understanding#

“Look at the average of 16 neighbors”
Just like during an exam, you not only refer to the answers of the nearest 4 classmates but also refer to the answers of 16 smart classmates around, making the answer more precise! 😂

🖼 Example#

• The calculation method is similar to bilinear interpolation but considers more pixels, making the image smoother.

🔹 Characteristics:

• Smoother than bilinear interpolation, suitable for high-definition image resizing.
• However, it has a larger computational load and is slower than bilinear interpolation.

🔸 4. Lanczos Interpolation#

🧐 Intuitive Understanding#

“Ask an AI expert to help you fill in the details”
Just like during an exam, instead of copying a classmate's answer, you ask AI to generate the optimal answer, but the computation time will be longer! 😆

🔹 Characteristics:

• Highest quality, with the sharpest edges.
• Slowest computation, suitable for medical imaging, ultra-clear photos.

📌 Which Interpolation Method is Best?#

📌 Code Example#

If you are using torchvision.transforms.Resize() in PyTorch for interpolation:

from torchvision import transforms
from torchvision.transforms import InterpolationMode

transform = transforms.Resize((256, 256), interpolation=InterpolationMode.BILINEAR)  # Bilinear Interpolation

If you are in OpenCV:

import cv2

# Read image
img = cv2.imread('image.jpg')

# Perform interpolation
img_resized = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)  # Bilinear Interpolation

🎯 Conclusion#

• Nearest Neighbor Interpolation (NEAREST): Fastest, but severely pixelated, suitable for semantic segmentation masks.
• Bilinear Interpolation (BILINEAR): Smooth, suitable for ordinary image resizing.
• Bicubic Interpolation (BICUBIC): Richer in detail, but slower computation, suitable for high-definition images.
• Lanczos Interpolation: Highest quality, but slowest computation, suitable for medical imaging, high-quality images.

This article is synchronized and updated by Mix Space to xLog. The original link is https://blog.kanes.top/posts/ArtificialIntelligence/Interpolation