Abstract
High-resolution image reconstruction arises in many applications, such as remote sensing, surveillance, and medical imaging. The Bose and Boo (1998) model can be viewed as the passage of the high-resolution image through a blurring kernel built from the tensor product of a univariate low-pass filter of the form [1/2 + ε, 1,..., 1, 1/2 - ε], where e is the displacement error. When the number L of low-resolution sensors is even, tight-frame symmetric framlet filters were constructed (Chan et al., 2004b) from this low-pass filter using Ron and Shen's (1997) unitary extension principle. The framelet filters do not depend on e, and hence the resulting algorithm reduces to that of the case where ε = 0. Furthermore, the framelet method works for symmetric boundary conditions. This greatly simplifies the algorithm. However, both the design of the tight framelets and extension to symmetric boundary are only for even L and cannot, be applied to the case when L is odd. In this article, we design tight framelets and derive a tight-framelet algorithm with symmetric boundary conditions that work for both odd and even L An analysis of the convergence of the algorithms is also given. The details of the implementations of the algorithm are also given.
Original language | English (US) |
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Pages (from-to) | 91-104 |
Number of pages | 14 |
Journal | International Journal of Imaging Systems and Technology |
Volume | 14 |
Issue number | 3 |
DOIs | |
State | Published - Jan 1 2004 |
Externally published | Yes |
Keywords
- Frame-lets
- High-Resolution image reconstruction
- Wavelets
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Software
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering