Second-Order Directional Total Generalized Variation Regularization for Image Super-resolution

被引：0
|
作者：
Wu Z.-H. [1 ]
Sun M.-J. [2 ]
Gu Z.-S. [1 ]
Fan M.-Y. [1 ]
机构：
[1] No.38 Research Institute of China Electronics Technology Group Corporation, Hefei, 230088, Anhui
[2] Control Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, 264209, Shandong
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2017年 / 45卷 / 11期
关键词：
Directional total generalization variation; Optimization algorithm; Regularization constrains; Super-resolution;
D O I：
10.3969/j.issn.0372-2112.2017.11.008
中图分类号：
O43 [光学]; T [工业技术];
学科分类号：
070207 ; 08 ; 0803 ;
摘要：
Super-resolution (SR) image reconstruction has developed into a powerful tool to enhance the image resolution for the systems with low-cost imaging sensors.A direct but efficient approach to super-resolve a low-resolution image is based on prior knowledge learning.But the existing methods do not consider matched high-level features in the images with structured edges, resulting in some smooth image artifacts.A second-order directional total generalized variation (DTGV) regularization based method is proposed to explore the underlying high-level information of the data in this paper.More specifically, second-order DTGV acts as not only an additional prior but also an effective constraint to reduce the image artifacts and remove the noise.Results from several texture images demonstrate that the proposed approach can generate high-resolution image details and tend to produce high-frequency textures. © 2017, Chinese Institute of Electronics. All right reserved.
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收藏
页码：2625 / 2632
页数：7
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