4.7 Article

Super Resolution Image Reconstruction Through Bregman Iteration Using Morphologic Regularization

Journal

IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 21, Issue 9, Pages 4029-4039

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2012.2201492

Keywords

Bregman iteration; deblurring; morphologic regularization; operator splitting; subgradients

Funding

  1. DST, GOI under the Indian Digital Heritage-Hampi Project

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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.

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