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MAP fusion method for superresolution of images with locally varying pixel quality
Author(s) -
Kim Kio,
Neretti Nicola,
Intrator Nathan
Publication year - 2008
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20137
Subject(s) - computer science , superresolution , pixel , artificial intelligence , computer vision , algorithm , kalman filter , filter (signal processing) , image (mathematics) , image quality , image resolution , set (abstract data type) , computation , interpolation (computer graphics) , programming language
Superresolution is a procedure that produces a high‐resolution image from a set of low‐resolution images. Many of superresolution techniques are designed for optical cameras, which produce pixel values of well‐defined uncertainty, while there are still various imaging modalities for which the uncertainty of the images is difficult to control. To construct a superresolution image from low‐resolution images with varying uncertainty, one needs to keep track of the uncertainty values in addition to the pixel values. In this paper, we develop a probabilistic approach to superresolution to address the problem of varying uncertainty. As direct computation of the analytic solution for the superresolution problem is difficult, we suggest a novel algorithm for computing the approximate solution. As this algorithm is a noniterative method based on Kalman filter‐like recursion relations, there is a potential for real‐time implementation of the algorithm. To show the efficiency of our method, we apply this algorithm to a video sequence acquired by a forward looking sonar system. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 242–250, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).