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Medientyp: Buch Titel: Motion deblurring : algorithms and systems Beteiligte: Rajagopalan, A. N [Sonstige Person, Familie und Körperschaft]; Chellappa, Rama [Sonstige Person, Familie und Körperschaft] Erschienen: Cambridge: Cambridge University Press, 2014 Umfang: XIV, 293 S.; Ill., graph. Darst; 26 cm Sprache: Englisch ISBN: 1107044367; 9781107044364 RVK-Notation: ST 330 : Bildverarbeitung und Mustererkennung Schlagwörter: Image processing Mathematics ; Image processing Digital techniques ; Digital video Editing Mathematics ; Photography Retouching Data processing ; Motion Mathematical models ; Aufsatzsammlung Entstehung: Anmerkungen: Includes bibiographical references and index Beschreibung: Machine generated contents note: 1.Mathematical models and practical solvers for uniform motion deblurring / Jiaya Jia -- 1.1.Non-blind deconvolution -- 1.2.Blind deconvolution -- 2.Spatially-varying image deblurring / Richard Szeliski -- 2.1.Review of image deblurring methods -- 2.2.A unified camera-shake blur model -- 2.3.Single image deblurring using motion density functions -- 2.4.Image deblurring using inertial measurement sensors -- 2.5.Generating sharp panoramas from motion-blurred videos -- 2.6.Discussion -- 3.Hybrid-imaging for motion deblurring / Shree K. Nayar -- 3.1.Introduction -- 3.2.Fundamental resolution tradeoff -- 3.3.Hybrid-imaging systems -- 3.4.Shift-invariant PSF image deblurring -- 3.5.Spatially-varying PSF image deblurring -- 3.6.Moving object deblurring -- 3.7.Discussion and summary -- 4.Efficient, blind, spatially-variant deblurring for shaken images / Jean Ponce -- 4.1.Introduction -- 4.2.Modelling spatially-variant camera-shake blur -- Contents note continued: 4.3.The computational model -- 4.4.Blind estimation of blur from a single image -- 4.5.Efficient computation of the spatially-variant model -- 4.6.Single-image deblurring results -- 4.7.Implementation -- 4.8.Conclusion -- 5.Removing camera shake in smartphones without hardware stabilization / Jan Flusser -- 5.1.Introduction -- 5.2.Image acquisition model -- 5.3.Inverse problem -- 5.4.Pinhole camera model -- 5.5.Smartphone application -- 5.6.Evaluation -- 5.7.Conclusions -- 6.Multi-sensor fusion for motion deblurring / Jlngyi Yu -- 6.1.Introduction -- 6.2.Hybrid-speed sensor -- 6.3.Motion deblurring -- 6.4.Depth map super-resolution -- 6.5.Extensions to low-light imaging -- 6.6.Discussion and summary -- 7.Motion deblurring using fluttered shutter / Amit Agrawal -- 7.1.Related work -- 7.2.Coded exposure photography -- 7.3.Image deconvolution -- 7.4.Code selection -- 7.5.Linear solution for deblurring -- 7.6.Resolution enhancement -- Contents note continued: 7.7.Optimized codes for PSF estimation -- 7.8.Implementation -- 7.9.Analysis -- 7.10.Summary -- 8.Richardson-Lucy deblurring for scenes under a projective motion path / Michael S. Brown -- 8.1.Introduction -- 8.2.Related work -- 8.3.The projective motion blur model -- 8.4.Projective motion Richardson--Lucy -- 8.5.Motion estimation -- 8.6.Experiment results -- 8.7.Discussion and conclusion -- 9.HDR imaging in the presence of motion blur / A.N. Rajagopalan -- 9.1.Introduction -- 9.2.Existing approaches to HDRJ -- 9.3.CRF, irradiance estimation, and tone-mapping -- 9.4.HDR imaging under uniform blurring -- 9.5.HDRI for non-uniform blurring -- 9.6.Experimental results -- 9.7.Conclusions and discussions -- 10.Compressive video sensing to tackle motion blur / Dikpal Reddy -- 10.1.Introduction -- 10.2.Related work -- 10.3.Imaging architecture -- 10.4.High-speed video recovery -- 10.5.Experimental results -- 10.6.Conclusions -- Contents note continued: 11.Coded exposure motion deblurring for recognition / Scott McCloskey -- 11.1.Motion sensitivity of iris recognition -- 11.2.Coded exposure -- 11.3.Coded exposure performance on iris recognition -- 11.4.Barcodes -- 11.5.More general subject motion -- 11.6.Implications of computational imaging for recognition -- 11.7.Conclusion -- 12.Direct recognition of motion-blurred faces / Rama Chellappa -- 12.1.Introduction -- 12.2.The set of all motion-blurred images -- 12.3.Bank of classifiers approach for recognizing motion-blurred faces -- 12.4.Experimental evaluation -- 12.5.Discussion -- 13.Performance limits for motion deblurring cameras / Mohit Gupta -- 13.1.Introduction -- 13.2.Performance bounds for flutter shutter cameras -- 13.3.Performance bound for motion-invariant cameras -- 13.4.Simulations to verify performance bounds -- 13.5.Role of image priors -- 13.6.When to use computational imaging -- 13.7.Relationship to other computational imaging systems -- Contents note continued: 13.8.Summary and discussion