Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
---|---|---|---|---|---|
Searching... | 30000010334290 | TA1637.5 M68 2014 | Open Access Book | Book | Searching... |
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Summary
Summary
A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research. It encompasses both algorithms and architectures, providing detailed coverage of practical techniques by leading researchers. From an algorithms perspective, blind and non-blind approaches are discussed, including the use of single or multiple images; projective motion blur model; image priors and parametric models; high dynamic range imaging in the irradiance domain; and image recognition in blur. Performance limits for motion deblurring cameras are also presented. From a systems perspective, hybrid frameworks combining low-resolution-high-speed and high-resolution-low-speed cameras are described, along with the use of inertial sensors and coded exposure cameras. Also covered is an architecture exploiting compressive sensing for video recovery. A valuable resource for researchers and practitioners in computer vision, image processing, and related fields.
Table of Contents
1 Mathematical models and practical solvers for uniform motion deblurringJiaya Jia |
2 Spatially varying image deblurringNeel Joshi and Sing Bing Kang and Richard Szeliski |
3 Hybrid-imaging for motion deblurringMoshe Ben-Ezra and Yu-Wing Tai and Michael Brown and Shree Nayar |
4 Removing camera shake in smart phones without hardware stabilizationFilip Sroubek and Jan Flusser |
5 Richardson-Lucy deblurring for scenes under a projective motion pathYu-Wing Tai and Michael Brown |
6 Multi-sensor fusion for motion deblurringJingyi Yu |
7 Flutter-shutter cameras for motion deblurringAmit Agrawal |
8 Efficient, blind, spatially-variant deblurring for shaken imagesOliver Whyte and Josef Sivic and Andrew Zisserman and Jean Ponce |
9 Coded-exposure motion deblurring for recognitionScott McCloskey |
10 HDR imaging in the presence of motion blurC. S. Vijay and C. Paramanand and A. N. Rajagopalan |
11 Compressive video sensing to tackle motion blurAshok Veeraraghavan |
12 Direct recognition of motion blurred facesKaushik Mitra and Priyanka Vageeswaran and Rama Chellappa |
13 Performance limits for motion deblurring camerasOlliver Cossairt and Mohit Gupta |