全局稀疏梯度耦合张量扩散的图像去噪模型

doi:10.3969/j.issn.1001-2400.2017.06.026

Abstract

Abstract:

A global sparse gradient coupled tensor diffusion model for image denoising is proposed to improve the problem of edges blurring induced by the DTTR model. First, a tensor matrix is constructed by the global sparse gradient which is more accurate and robust than classic gradient operators. Then the diffusion equation is guided by the tensor matrix for image denoising. The diffusion resulting from this model is isotropic inside a homogeneous region and anisotropic along its edge so that an accurate tracking of the edges is possible. Numerical experiments show that the proposed method achieves a competitive denoising performance in comparison with the comparative algorithms in terms of both subjective and objective qualities. Experimental results indicate that the performances of denoising methods can be improved by introducing a guide map, which is obtained by the global sparse gradient model.

ZHANG Rui;FENG Xiangchu;YANG Lixia;CHANG Lihong. Global sparse gradient coupled tensor diffusion model for image denoising[J].Journal of Xidian University, 2017, 44(6): 150-155.

References

［1］ DUAN H B, GAN L. Elitist Chemical Reaction Optimization for Contour-based Target Recognition in Aerial Images［J］. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5): 2845-2859.
［2］ KORYTKOWSKI M, RUTKOWSKI L, SCHERER R. Fast Image Classification by Boosting Fuzzy Classifiers［J］. Information Sciences, 2016, 327: 175-182.
［3］ PERONA P, MALIK J. Scale-space and Edge Detection Using Anisotropic Diffusion［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639.
［4］刘巧红, 李斌, 林敏. 非凸混合总变分图像盲复原［J］. 西安电子科技大学学报, 2016, 43(2): 120-125.
LIU Qiaohong, LI Bin, LIN Min. Non-convex Hybrid Total Variation Method for Image Blind Restoration［J］. Journal of Xidian University, 2016, 43(2): 120-125.
［5］马洪晋, 聂玉峰. 一种图像特征增强的各向异性扩散去噪方法［J］. 西安电子科技大学学报, 2015, 42(5): 154-160.
MA Hongjin, NIE Yufeng. Anisotropic Diffusion Denoising Method Based on Image Feature Enhancement［J］. Journal of Xidian University, 2015, 42(5): 154-160.
［6］ CHEN Y, LEVINE S E. Image Recovery via Diffusion Tensor and Time-delay Regularization［J］. Journal of Visual Communication and Image Representation, 2002, 13(1-2): 156-175.
［7］ PRASATH V B S, VOROTNIKOV D. On a System of Adaptive Coupled PDEs for Image Restoration［J］. Journal of Mathematical Imaging and Vision, 2014, 48(1): 35-52.
［8］ KARASEV P, KOLESOV I, FRITSCHER K, et al. Interactive Medical Image Segmentation Using PDE Control of Active Contours［J］. IEEE Transactions on Medical Imaging, 2013, 32(11): 2127-2139.
［9］ REYES J C D L, SCHONLIEB C B. Image Denoising: Learning the Noise Model via Nonsmooth PDE-constrained Optimization［J］. Inverse Problems & Imaging, 2013, 7(4): 1183-1214.
［10］ ZHANG K, ZUO W, CHEN Y, et al. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising［J］. IEEE Transactions on Image Processing, 2017, 26(7): 3142-3155.
［11］张瑞, 冯象初, 王斯琪, 等. 基于稀疏梯度场的非局部图像去噪算法［J］. 自动化学报, 2015, 41(9): 1542-1552.
ZHANG Rui, FENG Xiangchu, WANG Siqi, et al. A Sparse Gradients Field Based Image Denoising Algorithm via Non-local Means［J］. Acta Automatica Sinica, 2015, 41(9): 1542-1552.
［12］ RAGUET H, FADILI J, PEYRE G. A Generalized Forward-backward Splitting［J］. SIAM Journal on Imaging Sciences, 2013, 6(3): 1199-1226.
［13］ WANG Z, BOVIK A C, SHEIKH H R, et al. Image Quality Assessment: from Error Visibility to Structural Similarity［J］. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

Global sparse gradient coupled tensor diffusion model for image denoising

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 0

Metrics

Comments

Recommended 0