In this chapter, we have seen how the SciPy stack helps us solve many problems in Mathematical Imaging, from the effective representation of digital images, to their efficient storage, compression, and processing, modifying, restoring, or analyzing them. Although certainly exhaustive, this chapter scratches but the surface of this challenging field of engineering. One could easily write another 400 pages just devoted to this subject, and I invite you to further study the possibilities of the module scipy.ndimage, the imaging toolkit skimage, and the Python bindings for the libraries OpenCV or SimpleITK.

The chapter also closes my vision of what mastering the SciPy stack means. In truth, this vision only focused on the relational aspect between the scientific applications and the mathematical theory behind the needed routines. No efforts have been put forth to tackle the techniques of speeding up the codes by binding with other languages, for example. While this is an interesting and relevant topic, I defer to other more technical monographs on computer science for that aspect.