Computational Photography

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.

Dr. Rastislav Lukac is an accomplished digital imaging scientist with more than 10 years of advanced experience in conducting scholarly and applied research. In his professional career, he has held appointments at various leading organizations and research institutions, including the University of Toronto, Canada; the University of Amsterdam, Netherlands; the Aristotle University of Thessaloniki, Greece, and Epson Canada Ltd., Toronto. Since August 2009, he has been a senior digital imaging scientist - image processing manager at Foveon, Inc./Sigma Corp., San Jose, California, USA. He has authored and contributed to numerous books and textbooks, and he has published more than 200 scholarly research papers in the areas of digital camera image processing, color image and video processing, multimedia security, and microarray image processing. He is also author of more than 25 patent-pending inventions in the areas of digital color imaging and pattern recognition, and he has been cited more than 650 times in peer-review journals covered by the Science Citation Index (SCI). Among his many accolades, he was the recipient of the 2003 North Atlantic Treaty Organization / National Sciences and Engineering Research Council of Canada (NATO/NSERC) Science Award, and he received the Most Cited Paper Award for the Journal of Visual Communication and Image Representation for the years 2005-2007. He also authored the number one article in the ScienceDirect Top 25 Hottest Articles in Signal Processing for April-June 2008.

Single Capture Image Fusion. Single Capture Image Fusion with Motion Consideration. Lossless Compression of Bayer Color Filter Array Images. Color Restoration and Enhancement in the Compressed Domain. Principal Component Analysis-Based Denoising of Color Filter Array Images. Regularization-Based Color Image Demosaicking. Super-Resolution Imaging. Image Deblurring Using Multi-Exposed Images. Color High Dynamic Range Imaging: Algorithms for Acquisition and Display. High Dynamic Range Imaging for Dynamic Scenes. Shadow Detection in Digital Images and Videos. Document Image Rectification Using Single-View or Two-View Camera Input. Bilateral Filter: Theory and Applications. Painterly Rendering. Machine Learning Methods for Automatic Image Colorization. Machine Learning for Digital Face Beautification. High-Quality Light Field Acquisition and Processing. Dynamic View Synthesis with an Array of Cameras.