CVPR97-Local-Blur-Estimation-and-Super-Resolution--Chiang-Boult

CVPR97-Local-Blur-Estimation-and-Super-Resolution--Chiang-Boult

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Local Blur Estimation and Super-Resolution Ming-Chao Chiang Terrance E. Boult Columbia University Lehigh University Department of Computer Science Department of EECS New York, NY 10027 Bethlehem, PA 18015 chiang@cs.columbia.edu tboult@eecs.lehigh.edu Abstract Until now, all super-resolution algorithms have pre- sumed that the images were taken under the same illumina- tion conditions. This paper introduces a new approach to super-resolution—based on edge models and a local blur estimate—which circumvents these difficulties. The paper presents the theory and the experimental results using the new approach. 1 Introduction We have recently proposed a new algorithm for enhanc- ing image resolution from an image sequence [1] where we compared our super-resolution results with earlier re- search on this subject [2, 3, 4, 5, 6, 7, 8]. We showed that the integrating resampler [9] can be used to enhance image resolution. We further showed that warping techniques can have a strong impact on the quality of the super-resolution imaging. However, left unaddressed in [1] are several im- portant issues. Of particular interest is lighting variation. The objective of this paper is to address techniques to deal with these issues. The examples herein use text because the high frequency information highlights the difference in super-resolution algorithms and because it allows us to ignore, for the time being, the issues of 3D edges under different views. Clearly, matching is a critical component if this is to be used for fusing 3D objects under different viewing/lighting conditions. This paper, however, concen- trates on how well we can combine them presuming good matching information. In what follows in this paper, we first present the new super-resolution algorithm where we compare the resulting super-resolution images with those presented in [1] in Sec- tion 2. We then review the integrating resampler, an effi- cient method for warping using imaging-consistent restora- tion/reconstruction algorithms, in Section 3. This algo- rithm is well suited for today’s pipelined micro-processors. In addition, the integrating resampler can allow for modi- fications of the intensity values to better approximate the warping characteristics of real sensors. The new algo- rithms for edge localization and local blur estimation are This work is supported in part by NSF PYI IRI-90-57951, NSF grant CDA-9413782, and DOD MURI program ONR N00014-95-1-0601. Sev- eral other agencies and companies have also supported parts of this research. given, respectively, in Sections 4 and 5. These two algo- rithms are both based on the imaging-consistent restora- tion/reconstruction algorithms described in [10]. Future work is given in Section 6. 2
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This note was uploaded on 04/22/2010 for the course MI IP taught by Professor Vladbalan during the Spring '10 term at Universidad del Rosario.

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CVPR97-Local-Blur-Estimation-and-Super-Resolution--Chiang-Boult

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