基于分数阶与NCTGV的图像放大新模型

谢斌; 刘壮; 丁成军

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基于分数阶与NCTGV的图像放大新模型

更新时间：2026-09-04

- 基于分数阶与NCTGV的图像放大新模型
- A New Image Zooming Model Based on Fractional Order and NCTGV
- 微电子学与计算机 2018年35卷第1期页码：50-55
- 作者机构：
  
  1. 江西理工大学机构信息工程学院,江西,赣州,341000
  2. 深圳大学机构信息工程学院,广东,深圳,518060
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61363076江西省自然科学基金 20142BAB207020
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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谢斌, 刘壮, 丁成军. 基于分数阶与NCTGV的图像放大新模型[J]. 微电子学与计算机, 2018, 35(1): 50-55.

XIE Bin, LIU Zhuang, DING Cheng-jun. A New Image Zooming Model Based on Fractional Order and NCTGV[J]. 2018, 35(1): 50-55.
谢斌, 刘壮, 丁成军. 基于分数阶与NCTGV的图像放大新模型[J]. 微电子学与计算机, 2018, 35(1): 50-55. DOI：

XIE Bin, LIU Zhuang, DING Cheng-jun. A New Image Zooming Model Based on Fractional Order and NCTGV[J]. 2018, 35(1): 50-55. DOI：

摘要

针对传统全变分在图像放大时容易产生"阶梯效应"与边缘模糊等问题

在此基础上考虑到非凸二阶总广义变分在抑制"阶梯效应"以及分数阶在保护图像细节信息的特性

提出了基于纹理分解的分数阶非凸二阶总广义变分图像放大模型.新模型将低分辨率图像分解为卡通与纹理两个部分

卡通部分为原始图像的平坦区域

使用传统全变分易产生"阶梯效应"

因此对卡通部分采用非凸二阶总广义变分模型处理.针对图像细节信息较多的纹理部分

采用分数阶变分模型处理

可更好的完善图像细节信息.对所提出的新模型

采用原始对偶与Chambolle投影算法进行数值求解.数值数值实验结果表明文中所提的新模型在峰值信噪比与均方误差均等指标有明显的提升.

Abstract

Prone tothe staircase effect and the blurring of the traditional variational image magnification

on the basis of considering the non convex order two total generalized variation in the suppression of the staircase effect and the fractional order in preserving the image details the characteristics of non convex total two order generalized variational model of fractional texture image magnification based on decomposition is proposed. The new model decomposes the low resolution image into two parts: cartoon and texture

because for a flat area

easy to produce the staircase effect

so the cartoon part uses the two order non convex total generalized variational model. In this paper

the fractional variational model is used to deal with the texture of the image with more details. In this paper

the primal dual and Chambolle projection algorithm are used to solve the new fractional non convex model. The results of numerical experiments show that the new model has a significant improvement in peak signal to noise ratio and mean square error.

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references

Rudin LI, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1): 259-268. DOI: 259

Chambolle A. An algorithm for total variation minimization and applications[J]. Journal of Mathematical Imaging and Vision, 2004, 20(1/2): 89-97. DOI: 89

Malgouyres F. Total variation based oversampling of noisy images[C]//Scale-Space and Morphology in Computer Vision, Lecture Notes in Computer Science, Vancouver, Canada, 2006(2106): 111-122.

Bredies K, Valkonen T. Inverse problems with second-order total generalized variation constraints[C] //Proceedings of Samp TA 2011-9th international conference on sampling theory and applications. Singapore, 2011.

Chambolle A, Pock T. A first-order primal-dual algorithm for convex problems with applications to imaging[J]. Journal of Mathematical Ima-ging and Vision, 2011, 40(1) : 120-145. DOI: 120

Bai J, Feng X C. Fractional-order anisotropic diffusion for image denoising[J]. IEEE Transactions on hnage Processing, 2007, 16(10) : 2492-2502. DOI: 2492

New York Odlubny I. Fractional differential equations[M]. New York: Academic Press, 1999.

Bredies K, Kunisch K, Pock T. Total generalized variation[J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 492-526. DOI: 492

Peter Ochs, Alexey Dosovitskiy, Thomas Brox, et al. An iterated l1 algorithm for non-smooth non-convex optimization in computer vision[J]. Computer Vision % Pattern Recognition, CVPR 2013.2013, 86 (11) : 1759-1766. DOI: 1759

Aujol J, Aubert G, Blanc-Feraud L, et al. Image decomposition into a bounded variation component and an oscillating component[J]. Math Imagingnd Vision, 2005, 22(1): 71-88. DOI: 71

Wang Z, Bovik A, Sheikh H. et al. Image quality visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 1-14. DOI: 1

吴玉莲, 冯象初, 姜东焕. 二阶总广义变分图像放大[J]. 光子学报, 2013, 41(6): 732-736. DOI: 732

刘亚男, 杨晓梅, 陈超楠. 基于全变分正则化的超分辨率图像重建[J]. 计算机科学, 2016, 43(5): 274-278. DOI: 274

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