564 pages | 296 B/W Illus.
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:
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.
… presents state-of-the-art and recent research trends in image processing technology and computational photography, and the fundamentals of the associated theory and methods, and outlines the foundations on which solutions for many interesting and challenging computational imaging problems rest. … recommended for researchers involved in computational image processing techniques or for graduate-level students in optics or computational photography.
—IEEE ELECTRICAL INSULATION, SEPT/OCT 2011, VOL 27, # 5
Single Capture Image Fusion, J.E. Adams, Jr., J.F. Hamilton, Jr., M. Kumar, E.O. Morales, R. Palum, and B.H. Pillman
Single Capture Image Fusion with Motion Consideration, J.E. Adams, Jr., A. Deever, J.F. Hamilton, Jr., M. Kumar, R. Palum, and B.H. Pillman
Lossless Compression of Bayer Color Filter Array Images. K.-H. Chung and Y.-H. Chan
Color Restoration and Enhancement in the Compressed Domain, J. Mukherjee and S.K. Mitra
Principal Component Analysis-Based Denoising of Color Filter Array Images, R. Lukac and L. Zhang
Regularization-Based Color Image Demosaicking, D. Menon and G. Calvagno
Super-Resolution Imaging, B.K. Gunturk
Image Deblurring Using Multi-Exposed Images, S.-W. Jung and S.-J. Ko
Color High Dynamic Range Imaging: Algorithms for Acquisition and Display, O. Pirinen, A. Foi, and A. Gotchev
High Dynamic Range Imaging for Dynamic Scenes, C. Loscos and K. Jacobs
Shadow Detection in Digital Images and Videos, C. Benedek and T. Sziranyi
Document Image Rectification Using Single-View or Two-View Camera Input, H. Il Koo and N. Ik Cho
Bilateral Filter: Theory and Applications, B.K. Gunturk
Painterly Rendering, G. Papari and N. Petkov
Machine Learning Methods for Automatic Image Colorization, G. Charpiat, I. Bezrukov, M. Hofmann, Y. Altun, and B. Scholkopf
Machine Learning for Digital Face Beautification, G. Dror
High-Quality Light Field Acquisition and Processing, C.-K. Liang and H.H. Chen
Dynamic View Synthesis with an Array of Cameras, R. Yang, H. Wang, and C. Zhang