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Super Resolution Image Reconstruction Through Bregman Iteration Using Morphologic Regularization
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DOI：10.1109/TIP.2012.2201492.png)
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摘要
En 中文
Multiscale morphological operators are studied extensively in the literature for image processing and feature extraction purposes. In this paper, we model a nonlinear regularization method based on multiscale morphology for edge-preserving super resolution (SR) image reconstruction. We formulate SR image reconstruction as a deblurring problem and then solve the inverse problem using Bregman iterations. The proposed algorithm can suppress inherent noise generated during low-resolution image formation as well as during SR image estimation efficiently. Experimental results show the effectiveness of the proposed regularization and reconstruction method for SR image.
Keyword:
Bregman iteration
deblurring
morphologic regularization
operator splitting
subgradients
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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