This book is a wonderful read about image processing. Having read most of the 1st edition (yes I'm reading all of the 3rd), I can say that this 3rd edition is larger (yet not insurmountable) with more examples, explanations, mathematical development and new material. I'm currently in the chapter about frequency domain processing and must say that the previous chapter about spatial domain processing is more robust, even now including a good introduction to fuzzy logic. Anyhow, with a good background in applied math and Matlab programming, this material is easy reading for me, but should also be accessible enough for anyone with a general technical background.

(This review refers to the second edition of the book)

This book is a simple and very well written introduction to Image Processing. This book starts off with the very basics of the subject. In fact the introduction is a bit too long and may be boring for some. The book contains many examples from different real world applications. In most chapters this book covers only the very basic techniques, so readers who want to study more advanced concepts will have to look elsewhere. For example, this book does not cover Canny edge detection which is probably the benchmark edge detection algorithm. The chapter on wavelets was refreshing; it concentrated more on how wavelets can be used for image processing and less on the math. The chapters on enhancement, restoration and color are elementary. The chapter on compression was quite good. The book ends with a few chapters on segmentation and pattern recognition. Overall, its a very good introductory textbook well suited for senior undergraduate/first year graduate students.

The preface of this book starts with a quote:

"When something can be read without effort, great effort has gone into its writing".

And once you start reading the book, you realize how much the book lives upto this. Everything is written clearly and importany points are stressed again and again in each topic until you become fully familiar with them. Topics are written with implementation in mind, as you can get started with writing your own code instantly.

The book makes the subject as interseting as it gets, and although some existing basic concepts of mathematics and statistics are quite helpful while reading, but you dont even have to be familiar with Digital Signal Processing to fully understand the contents.

Finally, I'd like to say that out of the few poeple that fully understand a concept, fewer have the gift to deliver their knowledge to others, and the authors of this book have that gift.

The preface of this book starts with a quote:
"When something can be read without effort, great effort has gone into its writing".
And once you start reading the book, you realize how much the book lives upto this. Everything is written clearly and importany points are stressed again and again in each topic until you become fully familiar with them. Topics are written with implementation in mind, as you can get started with writing your own code instantly.
The book makes the subject as interseting as it gets, and although some existing basic concepts of mathematics and statistics are quite helpful while reading, but you dont even have to be familiar with Digital Signal Processing to fully understand the contents.
Finally, I'd like to say that out of the few poeple that fully understand a concept, fewer have the gift to deliver their knowledge to others, and the authors of this book have that gift.

This book feeds the readers all the basic concepts of Image Processing. Very easily understandable and lucid in explanations. I would rate it one among the best EE books published so far.

I am giving this book 4 stars because it's quite descriptive and easy to follow. It covers some of the basic concepts behind digital image analysis and touches on the more general signal processing concepts, but it doesn't go very deep into the actual math. This may be what you want, but for me it was an assigned text for a 4th year undergrad course I took in college, and I found it inadequate. I felt like I could have written this book before I even started the class. At the same time, though, I think it would have been a great text for a more entry-level course.

If you want a text that goes in further detail and provides some math to chew on, I have to recommend the "Fundamentals of Digital Image Processing" by Anil K. Jain. It is also sold on Amazon, and I found it to be more appropriate for a 3rd year, 4th year, or Master's level course.

This is the best, most understandable image processing book I ever read. It is the only image processing book I've ever read in which I could immediately turn the concepts into code (IDL). I spent most of a Christmas vacation thinking it was a novel I couldn't put down. That's about as good as it gets for a technical book! Well done, Gentlemen.

This book is the best textbook on image processing for senior/graduate students majoring in engineering or computer science. Although a knowledge of calculus and linear algebra is presumed, it is a very accessible textbook. Chapters one and two consist of very basic background information. The concepts of linearity, pixel distance measures, spatial versus gray scale resolution, and zooming and shrinking are explained. Chapter 3 is about image inhancement in the spatial domain, and includes discussions on contrast enhancement, histogram processing and equalization, and histogram matching. The idea of filtering images via an NxN kernel mask is also introduced. Chapter 4 is about filtering in the frequency domain. The 2D Fourier transform is introduced and it is explained how filtering can take place using this transform. Chapter five discusses image restoration. This includes Weiner filtering and minimum mean square error filtering. Chapter six discusses color image processing. This chapter discusses the various color spaces - RGB, CMYK, HSI, and how the transforms mentioned up to this point in the book can be performed in color. Chapter 7 is about wavelets and multiresolution processing. This chapter is a good solid presentation of wavelets and their usage in image processing. I would suggest that anyone interested in this subject start here before they read another book, since the presentation is clearer here than in books dedicated to the subject. Chapter 8 is about image compression. Basics of information theory are discussed, and lossy as well as lossless methods of compression are discussed. As a good follow-on to the previous chapter, the role of wavelets in compression is discussed. Chapter 9 discusses morphological image processing, which is that field of image processing that relies on the systematic "fattening" and "thinning" of edges to enhance images. Chapters 10,11, and 12 are a sort of introduction to computer vision topics. Chapter 10 discusses how to segment an image. Chapter 11 is about image descriptors that quantify segmented portions of an image. Chapter 12 is about object recognition and even has a short section on statistical classifiers. This book is a joy to read, and will make the topic of image processing very clear. There are plenty of diagrams, formulas, and equations listed. There are no examples to speak of, but algorithms are clearly specified so that I don't think that the book suffers because of the lack of examples. All engineering textbooks should be this well written. I particularly recommend this book as a reference for students and practitioners of robotics, video processing, and computer vision, since there are image processing considerations in all of these fields that this book will clarify.