What does a Convolutional Neural Network (CNN) do? Why is it particularly effective in image processing?

No problem, let's talk about this topic in plain language.

First, let's talk about: What does a CNN do?

Imagine you're looking at a photo, say of a cat. How do you recognize it as a cat?

Your brain's reaction probably isn't to analyze millions of pixels of the entire image all at once. Instead, it first notices some "parts," such as:

"Hmm, two pointy ears."
"Furry texture."
"Whiskers."
"A long tail."

Finally, your brain combines these "parts" of information and concludes: "This is a cat!"

What a Convolutional Neural Network (CNN) does is very similar to this process. It's an algorithm that mimics our visual system, specifically designed to process grid-like data such as images. Its core task is to automatically and hierarchically extract various useful features from images (like those 'parts' mentioned above), and then use these features to complete tasks, such as:

Image classification: Is this a cat or a dog?
Object detection: How many people? How many cars? Where are they located in the image?
Facial recognition: Is this face John or Jane?
Autonomous driving: Identifying road signs, pedestrians, lane lines.

In short, a CNN is a very powerful 'image feature extractor'.

So why is it so good at image processing?

The reason CNNs are so powerful is primarily due to a few "unique tricks" that make them particularly well-suited for image processing:

1. Local Perception

Unlike older methods that immediately look at the entire image, CNNs mimic our eyes, using a small "perception window" (academically called a "convolutional kernel" or "filter") to scan the entire image piece by piece.

First layer: The initial "filters" are simple, responsible only for detecting the most basic features, such as edges, corners, and color blocks. After scanning, they output several "feature maps"; one map might highlight the positions of all horizontal lines, while another might show all green areas.
Second layer: Subsequent "filters" no longer look at the original image, but rather at the "feature maps" output by the first layer. They combine simple features to form more complex ones. For example, if a "horizontal line" and a "vertical line" are found together, it recognizes a "corner"; if it sees an arc, it identifies the "outline of an eye".
And so on: Layer by layer, features become increasingly complex and abstract. From "edges" to "outlines", then to "eyes", "noses", and finally to "faces".

This hierarchical structure, moving from local to global, from simple to complex, perfectly aligns with how we perceive the world, making it naturally adept at processing images.

CNN Layers (Image source: aayushmnit's blog)

2. Parameter Sharing

This is one of CNN's most ingenious designs.

Imagine you want to teach a network to recognize a "bird's beak". A bird's beak might appear in the top-left corner of an image, or in the bottom-right.

The naive approach: Train a separate "bird's beak detector" for every possible location in the image. The computational cost would be astronomical!
CNN's clever approach: I only train one "bird's beak detector" (which is a "filter"). Once this detector learns what a bird's beak looks like, I use it to scan the entire image. No matter where the beak is, this detector can find it.

This "one filter scans everywhere" mechanism is parameter sharing. It drastically reduces the number of parameters that need to be learned, making training highly efficient and enabling the network to learn more general features.

3. Translation Invariance

This is a fantastic side effect of "parameter sharing".

Because the "filter" used to find features is the same, a CNN can recognize a cat whether it's on the left or right, top or bottom of the image. For it, the object's position doesn't matter; as long as the features that constitute the object appear, it's recognized. This insensitivity to position is called translation invariance, which is crucial in real-world image recognition.

In summary

You can imagine a CNN as a highly specialized "image analysis pipeline":

Raw material: An image.
Primary workstation: A group of workers (primary filters) are only responsible for finding the simplest lines and color blocks.
Intermediate workstation: Another group of workers (intermediate filters) take the semi-finished products from the primary workstation and assemble them into "parts" like "eyes" or "tires".
Advanced workstation: More advanced workers (advanced filters) piece together the parts into more complex components, such as "faces" or "car bodies".
Final assembly workshop: Finally, a "manager" (fully connected layer) looks at all the recognized advanced components and makes the final decision: "Hmm, it has a cat face, a cat body, confirmed to be a cat, ready for shipment!"

It is precisely this design of local perception, parameter sharing, and hierarchical progression that makes CNNs inherently "experts" in image processing, both efficient and powerful.