This text is a successor to the 1992 Mathematics for Computer Graphics. It retains the original Part I on plane geometry and pattern-generating symmetries, along with much on 3D rotation and reflection matrices. On the other hand, the completely new pages exceed in number the total pages of the older book.

In more detail, topology becomes a reference and is replaced by probability, leading to simulation, priors and Bayesian methods, and the Shannon Information Theory. Also, notably, the Fourier Transform appears in various incarnations, along with Artificial Neural Networks. As the book's title implies, all this is applied to digital images, their processing, compresssion, restoration and recognition.

Wavelets are used too, in compression (as are fractals), and in conjuction with B-splines and subdivision to achieve multiresolution and curve editing at varying scales. We conclude with the Fourier approach to tomography, the medically important reconstruction of an image from lower-dimensional projections.

As before, a high priority is given to examples and illustrations, and there are exercises, which the reader can use if desired, at strategic points in the text; these sometimes form part of the exercises placed at the end of each chapter. Exercises marked with a tick are partly, or more likely fully, solved on the website. Especially after Chapter 6, solutions are the rule, except for implementation exercises. In the latter regard there are a considerable number of pseudocode versions throughout the text, for example ALGO 11.9 of Chapter 11, simulating the d-dimensional Gaussian distribution, or ALGO 16.1, wavelet compression with limited percentage error.