Zoom into images pixel by pixel, explore RGB channels, and apply real image filters — discovering exactly how computers see and understand images!
Every image is a grid of tiny coloured squares called pixels. A 640×480 image has 307,200 pixels!
Each pixel has three numbers: Red (0–255), Green (0–255), Blue (0–255). These mix to make every colour.
A small matrix (kernel) slides over the image multiplying pixel values — this creates effects like blur, sharpen, and edge detection.
In deep learning, CNNs learn their own filters automatically from training data — detecting edges, textures, and shapes!
You explored pixels, separated RGB channels, and applied convolution filters!
The smallest element of a digital image. Each pixel stores one colour value — in RGB, that's three numbers.
Red, Green, Blue — each from 0–255. Mix all three at 255 to get white. All at 0 gives black.
A kernel matrix slides over the image. At each position, it multiplies and sums values, creating a transformed output.
Kernels that highlight rapid colour changes. The basis of how CNNs detect object outlines and shapes.
Shows the frequency of each brightness level. Used for image analysis, normalisation, and contrast adjustment.
Convolutional Neural Networks learn their own filters from data — detecting edges, textures, and complex patterns automatically.