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Super-Resolution Imaging

TLDR
In this paper, the authors proposed a generalized interpolation for super-resolution via image warping, which can be used for image enhancement using multiple apertures, as well as super resolution from Mutual Motion.
Abstract
Preface. Contributing Authors. 1. Introduction S. Chaudhuri. 2. Image Zooming: Use of Wavelets N. Kaulgud, U.D. Desai. 3. Generalized Interpolation for Super-Resolution D. Rajan, S. Chaudhuri. 4. High Resolution Image from Low Resolution Images B.C. Tom, et al. 5. Super-Resolution Imaging Using Blur as a Cue D. Rajan, S. Chaudhuri. 6. Super-Resolution via Image Warping T.E. Boult, et al. 7. Resolution Enhancement using Multiple Apertures T. Komatsu, et al. 8. Super-Resolution from Mutual Motion A. Zomet, S. Peleg. 9. Super-Resolution from Compressed Video C.A. Segall, et al. 10. Super-Resolution: Limits and Beyond S. Baker, T. Kanade. Index.

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Book

Image Alignment and Stitching: A Tutorial

TL;DR: In this article, the basic motion models underlying alignment and stitching algorithms are described, and effective direct (pixel-based) and feature-based alignment algorithms, and blending algorithms used to produce seamless mosaics.
Journal ArticleDOI

Super-resolution: a comprehensive survey

TL;DR: The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy, and common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super- resolution algorithms, and the most commonly employed databases are discussed.
Journal ArticleDOI

Image Super-Resolution by TV-Regularization and Bregman Iteration

TL;DR: A new time dependent convolutional model for super-resolution based on a constrained variational model that uses the total variation of the signal as a regularizing functional and an iterative refinement procedure based on Bregman iteration to improve spatial resolution is proposed.
Journal ArticleDOI

A super-resolution reconstruction algorithm for surveillance images

TL;DR: An edge-preserving maximum a posteriori (MAP) estimation based super-resolution algorithm using a weighted directional Markov image prior model for a ROI from more than one low-resolution surveillance image is proposed.
Proceedings ArticleDOI

Simultaneous super-resolution and feature extraction for recognition of low-resolution faces

TL;DR: This work proposes a new procedure for recognition of low-resolution faces, when there is a high-resolution training set available, and shows that recognition of faces of as low as 6 times 6 pixel size is considerably improved compared to matching using a super-resolution reconstruction followed by classification, and to matching with a low- resolution training set.
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