Covering ideas and methods while concentrating on fundamentals, this book includes wave motion; digital imaging; digital filtering; visualization aspects of the seismic reflection method; sampling theory; the frequency spectrum; synthetic seismograms; wavelet processing; deconvolution; seismic attributes; phase rotation; and seismic attenuation.

Blind image deconvolution is constantly receiving increasing attention from the academic as well the industrial world due to both its theoretical and practical implications. The field of blind image deconvolution has several applications in different areas such as image restoration, microscopy, medical imaging, biological imaging, remote sensing, astronomy, nondestructive testing, geophysical prospecting, and many others. Blind Image Deconvolution: Theory and Applications surveys the current state of research and practice as presented by the most recognized experts in the field, thus filling a gap in the available literature on blind image deconvolution. Explore the gamut of blind image deconvolution approaches and algorithms that currently exist and follow the current research trends into the future. This comprehensive treatise discusses Bayesian techniques, single- and multi-channel methods, adaptive and multi-frame techniques, and a host of applications to multimedia processing, astronomy, remote sensing imagery, and medical and biological imaging at the whole-body, small-part, and cellular levels. Everything you need to step into this dynamic field is at your fingertips in this unique, self-contained masterwork. For image enhancement and restoration without a priori information, turn to Blind Image Deconvolution: Theory and Applications for the knowledge and techniques you need to tackle real-world problems.

Fully updated, the second edition of this book covers the widespread advances in digital imaging technology, techniques, and devices. It discusses the increased power, storage capacity, and use of digital cameras, laptop computers, tablets, and cell phones in forensic science. It addresses methods for presenting evidence in a courtroom, including under Frye and Daubert rules. It also explains concepts with minimal jargon, making it accessible to a wide range of photography, criminal justice, forensic, and legal professionals.

Digital image processing has experienced explosive growth over the past two decades. Public awareness has increased by way of video games, digital video special effects used in the entertainment industry, as well as articles in the mainstream press. However, the most significant impact of digital image processing in the 90s will be in the area of applications to real-world problems. To help readers keep pace, author Kenneth R. Castleman concentrates on those techniques that have proven most useful in practice. Part I presents several important concepts that can be developed without detailed mathematical analysis for a basic understanding. Part II addresses techniques that rely more on mathematics and elaborates analytically on certain concepts introduced in Part I. Part III covers specific application areas that are particularly important in industry, science, and medicine.

Deconvolution is a technique in signal or image processing that is applied to recover information. When it is employed, it is usually because instrumental effects of spreading and blurring have obscured that information. In 1996, Deconvolution of Images and Spectra was published (Academic Press) as a second edition of Jansson's 1984 book, Deconvolution with Applications in Spectroscopy. This landmark volume was first published to provide both an overview of the field, and practical methods and results. The present Dover edition is a corrected reprinting of the second edition. It incorporates all the advantages of its predecessors by conveying a clear understanding of the field while providing a selection of effective, practical techniques. The authors assume only a working knowledge of calculus, and emphasize practical applications over topics of theoretical interest, focusing on areas that have been pivotal to the evolution of the most effective methods. This tutorial is essentially self-contained. Readers will find it practical and easy to understand.

This book discusses the use of digital image processing technologies in a wide range of industries. Using machine learning approaches, readers will get an understanding of the many technologies and tactics utilized in the digital image processing and the management of large amounts of data. Students and scholars in topics including engineering, agricultural, and medical image processing would find this book useful in further developing their expertise in these areas. This work is aimed at students with some background in mathematics or computer science, and it uses examples written in the programming language Matlab to introduce and clarify some of the fundamental ideas behind contemporary pattern recognition and image processing techniques. The focus is on application rather than theory, and the book provides a context for understanding the ideas through a sequence of carefully selected examples, activities, and computer experiments that rely on real-world applications in the fields of science, healthcare, and engineering. This book covers important topic like Understanding an image, Resolution and quantization, Bit-plane splicing, Pixels, Image restoration, Blind deconvolution, Shape iv transformation and homogeneous coordinates, Morphological processing, Dilation, erosion and structuring elements within Matlab, The hit-or-miss transformation, Image segmentation and many more.

The multi-billion dollar industry of digital imaging technology is an active research area with applications in our everyday lives in products such as digital cameras, scanners, printers and display systems. This book presents an introduction to the fundamentals of digital imaging, with emphasis on the basic operations of image capture and display of monochrome and colour images. The authors balance the mathematical description of real problems with practical examples. With a colour-plate section and real-world applications, this book is suitable for graduate students taking courses in digital imaging in electrical engineering and computer science departments. It will also be a useful reference for practitioners in industry.

This authoritative text (the second part of a complete MSc course) provides mathematical methods required to describe images, image formation and different imaging systems, coupled with the principle techniques used for processing digital images. It is based on a course for postgraduates reading physics, electronic engineering, telecommunications engineering, information technology and computer science. This book relates the methods of processing and interpreting digital images to the 'physics' of imaging systems. Case studies reinforce the methods discussed, with examples of current research themes. - Provides mathematical methods required to describe images, image formation and different imaging systems - Outlines the principle techniques used for processing digital images - Relates the methods of processing and interpreting digital images to the 'physics' of imaging systems

Computational photography refers broadly to imaging techniques that enhance or extend the capabilities of digital photography. This new and rapidly developing research field has evolved from computer vision, image processing, computer graphics and applied optics—and numerous commercial products capitalizing on its principles have already appeared in diverse market applications, due to the gradual migration of computational algorithms from computers to imaging devices and software. Computational Photography: Methods and Applications provides a strong, fundamental understanding of theory and methods, and a foundation upon which to build solutions for many of today's most interesting and challenging computational imaging problems. Elucidating cutting-edge advances and applications in digital imaging, camera image processing, and computational photography, with a focus on related research challenges, this book: Describes single capture image fusion technology for consumer digital cameras Discusses the steps in a camera image processing pipeline, such as visual data compression, color correction and enhancement, denoising, demosaicking, super-resolution reconstruction, deblurring, and high dynamic range imaging Covers shadow detection for surveillance applications, camera-driven document rectification, bilateral filtering and its applications, and painterly rendering of digital images Presents machine-learning methods for automatic image colorization and digital face beautification Explores light field acquisition and processing, space-time light field rendering, and dynamic view synthesis with an array of cameras Because of the urgent challenges associated with emerging digital camera applications, image processing methods for computational photography are of paramount importance to research and development in the imaging community. Presenting the work of leading experts, and edited by a renowned authority in digital color imaging and camera image processing, this book considers the rapid developments in this area and addresses very particular research and application problems. It is ideal as a stand-alone professional reference for design and implementation of digital image and video processing tasks, and it can also be used to support graduate courses in computer vision, digital imaging, visual data processing, and computer graphics, among others.