Fractional-order difference curvature-driven fractional anisotropic diffusion equation for image super-resolution

作     者：Xuehui Yin Shunli Chen Liping Wang Shangbo Zhou 

作者机构：School of Software and Engineering Chongqing University of Posts and Telecommunications Chongqing 400065P.R.China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of EducationChongqing University Chongqing 400030P.R.China College of Computer ScienceChongqing University Chongqing 400030P.R.China 

出 版 物：《International Journal of Modeling, Simulation, and Scientific Computing》 (建模、仿真和科学计算国际期刊（英文）)

年 卷 期：2019年第10卷第1期

页      面：177-189页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：This work was supported by National Natural Science Foundation of China(No.61701060) Major Project of Fundamental Science and Frontier Technology Research of Chongqing CSTC(Grant Nos.cstc2015jcyjBX0124 and cstc2015jcyjBX0090) Chongqing Research Program of Basic Research and Frontier Technology(No.cstc2017jcyjAX0007) Scientific and Technological Research Program of Chongqing Municipal Education Commission(No.KJ1600410) 

主　　题：Image super-resolution fractional differentiation difference curvature anisotropic diffusion 

摘      要：Image super-resolution methods-based existing edge indicating operators—namely Gauss curvature,mean curvature and gradient-cannot effectively identify the edges,ramps and flat regions and suffer from the loss of fine *** address these issues,this paper presents a fractional anisotropic diffusion equation based on a new edge indicator,named fractional-order difference curvature,which can characterize the intensity variations in *** introduce the frequency-domain definition for fractional-order derivative by the Fourier transform,which is easy to implement *** new edge indicator is better than the existing edge indicating operators in distinguishing between ramps and edges and can better handle the fine *** results for natural images validate that the proposed method can yield a visually pleasing result and better values of MSSIM and PSNR.