A previous blog post in this series highlighted the free circuits textbook from the Free Electrical Engineering Textbook Initiative. The initiative is a joint effort among experienced, highly respected professors from the Universities of Michigan, Utah, and California, Berkeley.
In addition to the circuits textbook, the initiative has produced a free image processing textbook, Image Processing for Engineers, available as a 438-page PDF file at ip.eecs.umich.edu. The book is co-authored by Dr. Andrew E. Yagle and Dr. Fawwaz T. Ulaby, both from the University of Michigan. Dr. Yagle is a Professor Emeritus of Electrical Engineering and Computer Science, and Dr. Ulaby is currently a professor in the ECE Division of the College of Engineering.
Figure 1: Image Processing for Engineers cover
The rapid proliferation of digital cameras and imaging sensors of all kinds has made image processing an increasingly important part of the electrical and computer engineering field. This is particularly true in medical, smart vehicle, security, and national defense applications, where both static and video images captured in various parts of the electromagnetic spectrum are now essential tools.
Like the Circuit Analysis and Design book, Image Processing for Engineers is a clearly-written and well-illustrated textbook. It is best suited for an upper-level undergraduate course or early graduate course because the reader is expected to know multi-variable calculus, advanced linear algebra, about one year of college-level statistics, Fourier transforms, and Laplacians.
The book’s twelve chapters cover the following topics:
- Imaging Sensors
- Review of 1-D Signals and Systems
- 2-D Images and Systems
- Image Interpolation
- Image Enhancement
- Deterministic Approach to Image Restoration
- Wavelets and Compressed Sensing
- Random Variables, Processes, and Fields
- Stochastic Denoising and Deconvolution
- Color Image Processing
- Image Recognition
- Supervised Learning and Classification
Possible topics for the authors to consider for future editions include basic video image processing, real-time imaging, and digital watermarks. Topics related to the physical “processing” of light waves at or even before the sensor, such as white balance, color appearance models, spherical aberration, and chromatic aberration might also be helpful. These topics are beyond the scope of the present text, but they are important to successful imaging.
In summary, Image Processing for Engineers is a mathematically sophisticated, clearly presented textbook on an important topic. Its text, graphics, exercises, and comprehensive companion Web site make it appropriate for adoption by universities and for self-study by working professionals.