The Best New Image Processing eBooks To Read In 2025

A widely used, classroom-tested text, Applied Medical Image Processing: A Basic Course delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field. Avoiding excessive mathematical formalisms, the book presents key principles by implementing algorithms from scratch and using simple MATLAB®/Octave scripts with image data and illustrations on an accompanying companion website.

Organized as a complete textbook, it provides an overview of the physics of medical image processing and discusses imaging physics, clinical applications of image processing, image formats and data storage, intensity transforms, filtering of images and applications of the Fourier transform, three-dimensional spatial transforms, volume rendering, image registration, tomographic reconstruction and basic machine learning. This Third Edition of the bestseller:. Contains a brand-new chapter on the basics of machine learning. Devotes more attention to the subject of color space. Includes additional examples from radiology, internal medicine, surgery, and radiation therapy. Incorporates freely available programs in the public domain (e.g., GIMP, 3DSlicer, and ImageJ) when applicableBeneficial to students of medical physics, biomedical engineering, computer science, applied mathematics, and related fields, as well as medical physicists, radiographers, radiologists, and other professionals, Applied Medical Image Processing: A Basic Course, Third Edition is fully updated and expanded to ensure a perfect blend of theory and practice. Wolfgang Birkfellner studied theoretical physics at, and holds a Ph.D in medical physics from, the University of Vienna, Austria. Currently, he is heading the Digital Image Processing Laboratory at the Center for Biomedical Engineering and Physics at the Medical University of Vienna. He is also a reviewer and editorial board member for major journals in the field, program committee member for international conferences, and principal investigator for several third-party funded research projects. Previously, he served as senior researcher at the University Hospital Basel/Switzerland and associate professor of medical physics at the Center for Biomedical Engineering and Physics of Vienna Medical School.

Following the successful publication of the 1st edition in 2009, the 2nd edition maintains its aim to provide an application-driven package of essential techniques in image processing and GIS, together with case studies for demonstration and guidance in remote sensing applications. The book therefore has a “3 in 1” structure which pinpoints the intersection between these three individual disciplines and successfully draws them together in a balanced and comprehensive manner.

The book conveys in-depth knowledge of image processing and GIS techniques in an accessible and comprehensive manner, with clear explanations and conceptual illustrations used throughout to enhance student learning. The understanding of key concepts is always emphasised with minimal assumption of prior mathematical experience. The book is heavily based on the authors’ own research. Many of the author-designed image processing techniques are popular around the world. For instance, the SFIM technique has long been adopted by ASTRIUM for mass-production of their standard “Pan-sharpen” imagery data. The new edition also includes a completely new chapter on subpixel technology and new case studies, based on their recent research.

"Introduction to Digital Image Processing" by Yajnavalkya Bandyopadhyay is an elaborate and organized guide which includes the fundamental concepts, techniques, and applications of digital image processing. Offering both a high-level summary of the field and deep insight into some of the methodologies and the challenges they encounter, this book provides a roadmap of the manipulations and analysis processes of digital images and the information content they extract. Digital image processing has wide applications across various fields.

The quality of images coming from MRI, CT scans, and X-rays is enhanced for better diagnosis and treatment planning in medical imaging. Satellite imagery is collected through digital image processing for activities like weather forecasting, environmental monitoring, and land use planning, helping to interpret land use-land cover features and trends over time. In computer vision, it is used in developing systems for facial recognition, autonomous vehicles, and industrial automation. Digital image processing techniques enhance image quality by adjusting contrast, brightness, and removing noise. The field faces challenges such as obtaining efficient algorithms to process large data in real-time, guaranteeing image processing quality, and addressing ethical and legal problems of image manipulation. Future improvements are likely to come with developments in machine learning and artificial intelligence, enabling more nuanced and exact image analysis. Moreover, combining digital image processing with technologies like AR and VR will create new possibilities and applications. Digital image processing is becoming an essential technology, used in a wide variety of applications that affect various facets of our life. "Introduction to Digital Image Processing" by Yajnavalkya Bandyopadhyay is written in a clear, comprehensive way, serving as a solid basis to understand the field, its techniques, and applications. This document not only provides a resource to learn but also powers the research and development that are long-term challenges for digital image processing.