Image super-resolution reconstruction algorithm based on fractional calculus

被引：1
|
作者：
Lei J. [1 ]
Wang H. [1 ]
Zhu L. [1 ]
Xiao J. [1 ]
机构：
[1] Electronic Information School, Wuhan University, Wuhan
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2017年 / 39卷 / 12期
关键词：
Fractional calculus; Projection onto convex set (POCS); Scale invariant feature transform (SIFT); Super-resolution;
D O I：
10.3969/j.issn.1001-506X.2017.12.31
中图分类号：
学科分类号：
摘要：
In order to solve the blurred image super-resolution reconstruction problem, the fractional calculus is combined with projection onto convex sets (POCS) to reconstruct super-resolution images. Using a fractional calculus operator to get the reference frame, through scale invariant feature transform (SIFT) matching, along with the fractional point spread function, and the imaging blurred images problem is solved by POCS, and the super-resolution reconstruction is implemented for noised or blurred images. Comparing with other super-resolution algorithms, experiments show that POCS with FC-optimized SIFT has good results in several objective evaluation indeces. Especially, when the origin low-resolution image is blurred, the algorithms based on contour stencils or sparse representation will increase the degree of blurring of the image, and the image obtained has obvious improvement in clarity and better reconstruction effect. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：2849 / 2856
页数：7
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