Image Fundamentals
Pixels, color spaces, image formats, and basic operations.
Color Spaces
A color space is a coordinate system that maps numerical tuples to perceivable colors, with different spaces optimizing for hardware convenience (RGB), perceptual uniformity (CIELAB), or separation of luminance from chrominance (YCbCr, HSV).
Convolution and Filtering
Convolution slides a small kernel (weight matrix) across an image, computing weighted sums at each position to achieve effects like blurring, sharpening, and edge detection – and is the same operation at the heart of convolutional neural networks.
Digital Images and Pixels
A digital image is a rectangular grid of discrete numerical values (pixels) that approximates a continuous visual scene through spatial sampling and intensity quantization.
Frequency Domain and Fourier Transform
The Fourier transform decomposes an image into a sum of sinusoidal components at different frequencies and orientations, enabling efficient filtering, compression, and analysis of periodic structures.
Image Histograms
An image histogram counts the frequency of each pixel intensity level, providing a compact statistical summary that drives contrast enhancement, thresholding, and exposure analysis.
Image Interpolation and Resampling
Image interpolation estimates pixel values at non-integer coordinates by combining nearby known samples, enabling image resizing, rotation, warping, and any geometric transformation that maps output pixels to fractional input positions.
Image Noise and Denoising
Image noise is unwanted random variation in pixel values introduced during capture or transmission, and denoising methods attempt to suppress it while preserving edges and detail – a fundamental tradeoff that runs through all of image processing.
Image Pyramids and Scale Space
Image pyramids and scale-space representations capture an image at multiple resolutions and blur levels, enabling algorithms to detect features and objects regardless of their size in the scene.
Morphological Operations
Morphological operations use a small structuring element to probe and modify the geometric structure of shapes in binary and grayscale images, enabling noise removal, shape analysis, and feature extraction through operations like erosion, dilation, opening, and closing.