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Single‐frame image super‐resolution using learned wavelet coefficients
Author(s) -
Jiji C. V.,
Joshi M. V.,
Chaudhuri Subhasis
Publication year - 2004
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.20013
Subject(s) - wavelet , regularization (linguistics) , computer science , artificial intelligence , smoothness , constraint (computer aided design) , frame (networking) , pixel , discontinuity (linguistics) , image (mathematics) , set (abstract data type) , term (time) , wavelet transform , computer vision , image resolution , pattern recognition (psychology) , mathematics , algorithm , mathematical analysis , physics , geometry , telecommunications , quantum mechanics , programming language
Abstract We propose a single‐frame, learning‐based super‐resolution restoration technique by using the wavelet domain to define a constraint on the solution. Wavelet coefficients at finer scales of the unknown high‐resolution image are learned from a set of high‐resolution training images and the learned image in the wavelet domain is used for further regularization while super‐resolving the picture. We use an appropriate smoothness prior with discontinuity preservation in addition to the wavelet‐based constraint to estimate the super‐resolved image. The smoothness term ensures the spatial correlation among the pixels, whereas the learning term chooses the best edges from the training set. Because this amounts to extrapolating the high‐frequency components, the proposed method does not suffer from oversmoothing effects. The results demonstrate the effectiveness of the proposed approach. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 105–112, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20013