Noniterative interpolation-based super-resolution minimizing aliasing in the reconstructed image

被引:40
作者
Sanchez-Beato, Alfonso [1 ]
Pajares, Gonzalo [2 ]
机构
[1] Univ Nacl Educ Distancia, Dept Informat & Automat, E-28040 Madrid, Spain
[2] Univ Complutense Madrid, Dept Ingn Software & Inteligencia Artificial, E-28040 Madrid, Spain
关键词
image reconstruction; inverse problem; nonuniform sampling; super-resolution;
D O I
10.1109/TIP.2008.2002833
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Super-resolution (SR) techniques produce a high-resolution image front a set of low-resolution undersampled images. In this paper, we propose a new method for super-resolution that uses sampling theory concepts to derive a noniterative SR algorithm. We first raise the issue of the validity of the data model usually assumed in SR, pointing out that it imposes a band-limited reconstructed image plus a certain type of noise. We propose a sampling theory framework with a prefiltering step that allows us to work with more general data models and also a specific new method for SR that uses Delaunay triangulation and B-splines to build the super-resolved image. The proposed method is noniterative and well posed. We prove its effectiveness against traditional iterative and noniterative SR methods on synthetic and real data. Additionally, we also prove that we can first solve the interpolation problem and then make the deblurring not only when the motion is translational but also when there are rotations and shifts and the imaging system Point Spread Function (PSF) is rotationally symmetric.
引用
收藏
页码:1817 / 1826
页数:10
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