Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10359626 | Image and Vision Computing | 2005 | 9 Pages |
Abstract
Super-resolution image reconstruction produces a high-resolution image from a set of shifted, blurred, and decimated versions thereof. Previously published papers have not addressed the computational complexity of this ill-conditioned large scale problem adequately. In this paper, the computational complexity of MAP-based multiframe super-resolution algorithms is studied, and a new fast algorithm, as well as methods for parallel image reconstruction is also presented. The proposed fast algorithm splits the multiple input low-resolution images into several subsets according to their translation relations, and then applies normal MAP algorithm to each subset, the reconstructed images are processed subsequently at a successive level until the desired resolution is achieved. Experiment results are also provided to demonstrate the efficiency of the proposed techniques.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Di Zhang, Huifang Li, Minghui Du,