期刊
JOURNAL OF SCIENTIFIC COMPUTING
卷 37, 期 3, 页码 367-382出版社
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10915-008-9214-8
关键词
Super-resolution; Total variation restoration; Bregman iteration; Downsampling; Edge preserving
资金
- DGICYT [MTM2005-07708]
- NSF [DMS-0312222, ACI-0321917]
- NIH [G54 RR021813]
In this paper we formulate 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. We propose an iterative refinement procedure based on Bregman iteration to improve spatial resolution. The model uses a dataset of low resolution images and incorporates a downsampling operator to relate the high resolution scale to the low resolution one. We present an algorithm for the model and we perform a series of numerical experiments to show evidence of the good behavior of the numerical scheme and quality of the results.
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