Digital Image Processing — Gonzalez & Woods

Rafael C. Gonzalez and Richard E. Woods, 4th Edition (Pearson)

The standard text on digital image processing. It carries the one-dimensional signal processing of the earlier phases into two dimensions: the image as a sampled, quantized 2-D signal, then spatial filtering (which is 2-D convolution), frequency-domain filtering (the 2-D DFT), and image restoration (the Wiener filter, applied to a blurred and noisy image). On that base it builds the algorithms that only exist in the image domain — compression and its entropy coders, segmentation, and feature extraction — and closes, in the 4th edition, on a substantial new chapter on neural networks and deep convolutional networks.

Worked in Course 1 — the digital image processing phase: Week 15 (fundamentals and spatial filtering, Ch. 2–3), Week 16 (frequency-domain filtering and restoration, Ch. 4–5), and Week 17 (compression, segmentation, and feature extraction, Ch. 8, 10–11), then Ch. 12 (image pattern classification and deep CNNs) as part of the learned-signal-processing capstone in Week 20. Every chapter carries a problem set, worked by hand.

Chapters

Exercises added as I work through each chapter.