基于改进复扩散自适应耦合非局部变换域模型的图像放大

doi:10.11772/j.issn.1001-9081.2017092273

计算机应用 ›› 2018, Vol. 38 ›› Issue (4): 1151-1156.DOI: 10.11772/j.issn.1001-9081.2017092273

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于改进复扩散自适应耦合非局部变换域模型的图像放大

海涛^1,2,3, 张雷^1,4, 刘旭焱^1,2, 张新刚⁵

1. 南阳师范学院机电工程学院, 河南南阳 473061;
2. 石油装备智能化控制河南省工程实验室(南阳师范学院), 河南南阳 473061;
3. 南阳师范学院图像处理与模式识别研究所, 河南南阳 473061;
4. 南阳师范学院物理与电子工程学院, 河南南阳 473061;
5. 南阳师范学院计算机与信息技术学院, 河南南阳 473061

收稿日期:2017-09-19 修回日期:2017-11-19 出版日期:2018-04-10 发布日期:2018-04-09
通讯作者: 海涛
作者简介:海涛(1974-),男,河南南阳人,讲师,博士,主要研究方向:图像分辨增强、非线性信号处理;张雷(1981-),男,河南南阳人,讲师,博士研究生,主要研究方向:红外图像处理、图像融合;刘旭焱(1983-),男,河南南阳人,副教授,博士,主要研究方向:纳米发光材料、电子器件;张新刚(1979-),男,河南漯河人,副教授,硕士,主要研究方向:网络信息安全。
基金资助:
国家自然科学基金资助项目（61702289）；河南省教育厅科学技术研究重点项目（14A520057，15B520022）；河南省高等学校重点科研项目（17A510016，16B510005）；南阳师范学院校级项目（ZX2015004）。

Image enlargement based on improved complex diffusion adaptivly coupled nonlocal transform domain model

HAI Tao^1,2,3, ZHANG Lei^1,4, LIU Xuyan^1,2, ZHANG Xingang⁵

1. School of Mechanical and Electrical Engineering, Nanyang Normal University, Nanyang Henan 473061, China;
2. Oil Equipment Intelligent Control Engineering Laboratory of Henan Province(Nanyang Normal University), Nanyang Henan 473061, China;
3. Institute of Image Processing and Pattern Recognition, Nanyang Normal University, Nanyang Henan 473061, China;
4. School of Physics and Electronic Engineering, Nanyang Normal University, Nanyang Henan 473061, China;
5. School of Computer and Information Technology, Nanyang Normal University, Nanyang Henan 473061, China

Received:2017-09-19 Revised:2017-11-19 Online:2018-04-10 Published:2018-04-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61702289), the Key Project of Science and Technology Research of Henan Provincial Education Department (14A520057, 15B520022), the Key Research Projects of Henan Colleges and Universities (17A510016, 16B510005), the Project of Nanyang Normal University (ZX2015004).

摘要/Abstract

摘要： 针对二阶偏微分方程（PDE）放大算法丢失弱边缘和纹理细节的不足，提出一种改进复扩散自适应耦合非局部变换域模型的图像放大算法。利用复扩散具有边缘定位准确的特点耦合冲击滤波器，改进复扩散模型能够较好地增强强边缘；而通过对相似图像块构成图像组的三维变换系数的稀疏特性进行建模，非局部变换域模型能够很好地利用图像中相似图像块的非局部信息，对弱边缘和纹理细节有较好的处理效果；最后利用复扩散得到图像的二阶导数作为参数实现改进复扩散模型和非局部变换域模型自适应耦合。所提算法与偏微分方程放大算法、非局部变换域放大算法和偏微分方程耦合空域非局部模型放大算法进行仿真实验比较，在强边缘、弱边缘和细节纹理具有较好的放大效果，弱边缘和纹理细节图像在平均结构相似性测度上高于改进复扩散放大算法、非局部变换域放大算法。所提算法验证了空域模型和变换域模型、局部模型和非局部模型耦合结合的有效性。

关键词: 非线性复扩散, 图像放大, 非局部变换域模型, 非局部自相似

Abstract: Concerning the loss of weak edges and texture details of the second-order Partial Differential Equation (PDE) amplification algorithm, an image enlargement algorithm was proposed based on improved complex diffusion adaptively coupled nonlocal transform domain model. By utilizing the advantage of accurate edge location of the complex diffusion model, the improved complex diffusion coupled impulse filter to enhance strong edges better; by modeling the sparse characteristics of the transform coefficients coming from three dimensional transformation of the image group composed of similar image blocks, the nonlocal transform domain model could make good use of the nonlocal information of the similar image blocks and had better processing effects on weak edges and texture details. Finally, the second-order derivation of the image obtained by the complex diffusion was used as the parameter to realize the adaptive coupling of the improved complex diffusion model and the nonlocal transform domain model. Compared with partial differential equation amplification algorithm, nonlocal transformation domain amplification algorithm and partial differential equation coupled space domain nonlocal model amplification algorithm, the proposed algorithm has better amplification effect on strong edges, weak edges and detail textures, the mean structural similarity measures of weak edges and texture detail images are higher than those of improved complex diffusion magnification algorithm and the nonlocal transform domain amplification algorithm. The proposed algorithm also confirms the validity of the coupling between the space domain model and the transform domain model, local model and nonlocal model.

Key words: anisotropic complex diffusion, image enlargement, nonlocal transform domain model, nonlocal self-similarity

中图分类号:

TN911

海涛, 张雷, 刘旭焱, 张新刚. 基于改进复扩散自适应耦合非局部变换域模型的图像放大[J]. 计算机应用, 2018, 38(4): 1151-1156.

HAI Tao, ZHANG Lei, LIU Xuyan, ZHANG Xingang. Image enlargement based on improved complex diffusion adaptivly coupled nonlocal transform domain model[J]. Journal of Computer Applications, 2018, 38(4): 1151-1156.

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基于改进复扩散自适应耦合非局部变换域模型的图像放大

Image enlargement based on improved complex diffusion adaptivly coupled nonlocal transform domain model

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