Image Processing Handbook · Digital image processing referenceSkip to content<br>Image processing,from photons to pipelines<br>A working reference for digital image processing: sampling, colour spaces, convolution, geometry, frequency analysis, compression and GPU pipelines.<br>Start readingJump to Module 01<br>09Modules<br>28Sections<br>00Runtime dependencies
01Foundations of Digital ImagesFrom photons to arrays: sampling, quantisation, channel layout, colour spaces, and how pixels sit in memory.Analog to DigitalAnatomy of an ImageColour Spaces and ConversionsMemory and Data Structures<br>02Point Operations and HistogramsPer-pixel mappings, lookup tables, histogram statistics, equalisation, and threshold selection.Pixel-wise TransformationsHistogram AnalysisThresholding and Binarisation<br>03Spatial Filtering and ConvolutionKernels, boundary handling, smoothing families, derivative operators, and sharpening.2D Discrete ConvolutionSmoothing and Noise ReductionEdge Detection and Sharpening<br>04Geometry and InterpolationAffine and projective mapping, resampling kernels, and the quality against cost trade-off.Affine and Perspective MappingInterpolation AlgorithmsPractical Applications<br>05Frequency Domain ProcessingThe discrete Fourier transform, amplitude and phase, and filters designed in frequency space.Fast Fourier TransformFrequency Filtering<br>06Morphology and ContoursStructuring elements, the four base operators, skeletons, contour hierarchies, and watershed segmentation.Binary MorphologyFeature Extraction and Analysis<br>07Compression and FormatsEntropy coding, transform coding, modern container formats, and palette quantisation.Lossless CompressionLossy CompressionColour Quantisation<br>08High-Performance Web ArchitectureTyped arrays, fragment shaders, worker offloading, and WebAssembly builds of native libraries.Low-Level Pixel ManipulationHardware AccelerationParallelism and WebAssembly<br>09Neural MethodsWhere learned models replace hand-written kernels: upscaling, segmentation, and inpainting.Classical Filters against Deep LearningSuper-ResolutionSegmentation and Background RemovalInpaintingDeployment notes