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Computer Science - Image Processing | Motion-Free Super-Resolution

Motion-Free Super-Resolution

Chaudhuri, Subhasis, Manjunath, Joshi

2005, XIII, 239 p.

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With the explosion of Internet technology and graphics engines, digital images are now everywhere.  Image capturing tools are all pervading - in our pockets to inside a satellite.  And although imaging applications demand an availability to high resolution images, such images are not picture perfect and may be lacking sufficient details.  This requires that these images be super-resolved for improved details.  How to achieve this is what constitutes research in the area of image super-resolution. 

Motion-Free Super-Resolution explores new technology for image super-resolution - applying cues other than the motion cue in super-resolving a scene.  This book will serve as an essential reference for both academecians and practicing engineers in the area of image processing and computer vision, as well as providing a basis for ongoing research in this field.

Content Level » Research

Keywords » Interpolation - MAP estimation - blind restoration - defocus phenomenon - image modeling - imaging - low-resolution image formation - modeling - performance - photometry - super resolution - wavelet coefficients - wavelets - zoom cue

Related subjects » Image Processing - Information Systems and Applications

Table of contents / Sample pages 

1. Image Super-Resolution 2. Review of Current Status 3. Use of Defocus Cue; Introduction; Defocus Phenomenon; Low-resolution image formation; Modeling of Image and Scene Depth; MAP Estimation; Demonstration of Results; Discussions 4. Photometry Based Method; Introduction; Photometry and Scene Structure; Generalized Interpolation; Inconsistency handling; Super-resolution Estimation; Experimental Demonstration; Discussions 5. Blind Restoration of Photometric Observartions; Introduction; Blind Restoration; Observation Model; Joint Restoration and Structure Recovery; Demonstration; Discussions 6. Wavelet Based Method; Introduction; Single-Frame Super-Resolution; Wavelets and Multiresolution Analysis; Learning of Wavelet Coefficients; Super-resolution Restoration; Experimental Performance; Discussions 7. Use of Zoom Cue; Introduction; Problem Definition; Zoom Estimation; Super-resolving a Scene with a Known Model; Learning of Model Parameters; Experimental Demonstration; Discussions 8. Looking Ahead

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