May 1, 2024
Updated May 9, 2025
16 minute read
An Introduction to Digital Imaging
Digital imaging is the process of creating a digital representation of a visual object, such as a physical scene or the internal structure of an object. This process involves capturing an image using a sensor, like those found in digital cameras or medical scanners, and then converting that information into a digital format that computers can process and display. At its core, digital imaging allows us to capture, store, manipulate, and share visual information with unprecedented ease and accuracy.
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Reading list
We've selected 26 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Digital Imaging.
This foundational textbook widely used in undergraduate and graduate programs. It provides a comprehensive introduction to the fundamental concepts and techniques of digital image processing, covering topics from image acquisition to segmentation and representation. It is an essential reference for anyone seeking a deep understanding of the core principles in the field. is commonly used as a primary textbook in academic institutions.
Provides a comprehensive overview of digital image processing, covering fundamental concepts, algorithms, and applications. The authors have extensive experience in the field, and the book is well-written and easy to follow.
Dives into the application of deep learning techniques for computer vision tasks, a highly contemporary area in digital imaging. It covers various deep learning models and their use in image classification, object detection, and more. It's valuable for those looking to understand the role of AI in modern digital imaging.
Provides a broad yet detailed overview of computer vision algorithms, many of which are built upon digital image processing fundamentals. It's suitable for both students and researchers and includes a wealth of algorithms and applications. It's a valuable resource for deepening understanding beyond basic image processing.
Covers the application of deep learning to computer vision systems, including image classification, object detection, and generative models. It provides a practical approach with code examples. It is highly relevant for understanding contemporary advancements in digital imaging driven by AI.
A widely recognized textbook for upper-level undergraduate and graduate courses in computer vision. It covers fundamental concepts and advanced topics with a focus on the underlying theory. is excellent for gaining a deeper understanding of how image processing techniques are applied in computer vision.
This handbook serves as a practical guide and comprehensive reference for a wide array of image processing techniques and procedures. It is richly illustrated and covers applications across various fields. is particularly useful as a reference tool for practitioners and researchers.
Covers a wide range of topics in computer vision, including image formation, feature detection, object recognition, and image segmentation. The author leading researcher in the field, and the book is well-written and up-to-date.
This text focuses specifically on digital image processing techniques as applied to medical images. It uses real medical examples to illustrate concepts and is suitable for advanced undergraduates and graduate students. It's a great resource for understanding a key application area of digital imaging.
This comprehensive text provides a strong foundation in pattern recognition and machine learning, which are increasingly integral to advanced digital image analysis and computer vision tasks. While not solely focused on imaging, it provides essential background knowledge for understanding many modern techniques. It widely respected reference in the field.
Building upon the fundamental techniques, this book explores more advanced methods in digital image processing. It's suitable for those who have a basic understanding and wish to deepen their knowledge of more complex algorithms and applications. adds significant depth for advanced students and professionals.
Provides a practical introduction to digital image processing using MATLAB. The authors provide clear explanations of the concepts and algorithms, and the book is full of examples and exercises.
While focused on medical imaging, this book provides a strong foundation in the physics and principles behind various medical imaging modalities (like X-ray, CT, MRI). Understanding image acquisition in these contexts is crucial for processing and analysis in this significant application area of digital imaging. It's a key reference for those interested in medical image processing.
Offers an accessible introduction to the essential topics in computer vision, highlighting key algorithms and mathematical concepts. It's suitable for undergraduates and provides a good bridge from basic image processing to computer vision. It includes exercises and review questions to solidify understanding.
Focuses on the signals and systems aspects of medical imaging, providing a different perspective on how medical images are formed and processed. It's a valuable resource for understanding the technical underpinnings of medical image processing and analysis. It complements more general image processing texts with a specific application focus.
Focuses on the fundamental techniques of digital image processing, providing a clear and accessible explanation of the core concepts. It's a good resource for gaining a solid understanding of the basic building blocks of image processing. It can serve as a strong starting point before delving into more advanced topics.
Covers the use of machine learning techniques in computer vision. The authors are leading researchers in the field, and the book provides a comprehensive overview of the latest advances.
Specifically addresses the analysis of digital images obtained from remote sensing platforms (like satellites and aerial photography). It covers techniques and applications relevant to environmental monitoring, land use, and resource management, showcasing a significant application domain of digital imaging.
Provides an introduction to the various medical imaging modalities from a physics and engineering perspective. Understanding how medical images are generated is fundamental to processing and analyzing them effectively. It's a good introductory text for those interested in the medical applications of digital imaging.
This practical book focuses on using the OpenCV library for computer vision and image processing tasks. It's an excellent resource for those who want to implement digital imaging techniques in code. It bridges the gap between theory and practical application, making it very useful for students and professionals.
Provides a practical introduction to digital image processing. The author covers a wide range of topics, including image acquisition, enhancement, and analysis. The book is well-written and easy to follow.
Offers practical tips and techniques for digital photography, focusing on the creative and technical aspects of capturing digital images. While not a theoretical text on image processing, it provides valuable context on how digital images are created and the factors influencing their quality. It's a good starting point for understanding the source of digital images.
Provides a comprehensive overview of computer vision. The authors cover a wide range of topics, including image formation, feature detection, object recognition, and image segmentation. The book is well-written and up-to-date.
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