Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image fro...Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image from its finite number of noisy compressive samples. A novel self-adaption, texture preservation method is designed to select the weight. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. The numerical examples are performed to compare its performance with four state-of-the-art algorithms. Experimental results clearly show that our method has better reconstruction accuracy on texture images than the existing scheme.展开更多
In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 0. The numerical experiments demonstrate tha...In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 0. The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models. In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models. For images with strong noises, the restored images of the compound algorithm are the best in the corresponding restored images. The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration. It is found that the combination of these methods is efficient and robust in the image restoration.展开更多
Neutron computed tomography(NCT)is widely used as a noninvasive measurement technique in nuclear engineering,thermal hydraulics,and cultural heritage.The neutron source intensity of NCT is usually low and the scan tim...Neutron computed tomography(NCT)is widely used as a noninvasive measurement technique in nuclear engineering,thermal hydraulics,and cultural heritage.The neutron source intensity of NCT is usually low and the scan time is long,resulting in a projection image containing severe noise.To reduce the scanning time and increase the image reconstruction quality,an effective reconstruction algorithm must be selected.In CT image reconstruction,the reconstruction algorithms can be divided into three categories:analytical algorithms,iterative algorithms,and deep learning.Because the analytical algorithm requires complete projection data,it is not suitable for reconstruction in harsh environments,such as strong radia-tion,high temperature,and high pressure.Deep learning requires large amounts of data and complex models,which cannot be easily deployed,as well as has a high computational complexity and poor interpretability.Therefore,this paper proposes the OS-SART-PDTV iterative algorithm,which uses the ordered subset simultaneous algebraic reconstruction technique(OS-SART)algorithm to reconstruct the image and the first-order primal–dual algorithm to solve the total variation(PDTV),for sparse-view NCT three-dimensional reconstruction.The novel algorithm was compared with other algorithms(FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV)by simulating the experimental data and actual neutron projection experiments.The reconstruction results demonstrate that the proposed algorithm outperforms the FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV algorithms in terms of preserving edge structure,denoising,and suppressing artifacts.展开更多
In[3],Chan and Wong proposed to use total variational regularization for both images and point spread functions in blind deconvolution.Their experimental results show that the detail of the restored images cannot be r...In[3],Chan and Wong proposed to use total variational regularization for both images and point spread functions in blind deconvolution.Their experimental results show that the detail of the restored images cannot be recovered.In this paper,we consider images in Lipschitz spaces,and propose to use Lipschitz regularization for images and total variational regularization for point spread functions in blind deconvolution.Our experimental results show that such combination of Lipschitz and total variational regularization methods can recover both images and point spread functions quite well.展开更多
Image segmentation is a significant problem in image processing.In this paper,we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation(FBTV).The first stage of our method is t...Image segmentation is a significant problem in image processing.In this paper,we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation(FBTV).The first stage of our method is to calculate a smooth solution from the FBTV Mumford-Shah model.Furthermore,we design a new difference of convex algorithm(DCA)with the semi-proximal alternating direction method of multipliers(sPADMM)iteration.In the second stage,we make use of the smooth solution and the K-means method to obtain the segmentation result.To simulate images more accurately,a useful operator is introduced,which enables the proposed model to segment not only the noisy or blurry images but the images with missing pixels well.Experiments demonstrate the proposed method produces more preferable results comparing with some state-of-the-art methods,especially on the images with missing pixels.展开更多
Color image segmentation is crucial in image processing and computer vision.Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore th...Color image segmentation is crucial in image processing and computer vision.Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels,which contain some key feature information of the image.To better describe the relationship of color channels,we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively.Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation.The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space.Moreover,to accelerate the optimization process,we use a new primal-dual algorithm to solve our novel model.Numerical results demonstrate clearly that the performance of our proposed method is excellent.展开更多
三维点云数据建模是三维重建的核心技术,广泛应用于医学、工业检测及文化遗产保护等领域。文章系统梳理了点云数据的结构与获取方法(如激光扫描、多视图成像),重点分析了基于隐式表示法的表面重建算法。该类算法通过隐式函数描述三维表...三维点云数据建模是三维重建的核心技术,广泛应用于医学、工业检测及文化遗产保护等领域。文章系统梳理了点云数据的结构与获取方法(如激光扫描、多视图成像),重点分析了基于隐式表示法的表面重建算法。该类算法通过隐式函数描述三维表面,擅长处理复杂的拓扑结构,但在面对噪声、稀疏数据及复杂场景时存在局限。为解决这些问题,文章提出了一种基于总变差(Total Variation,TV)正则化的改进算法,利用TV项抑制噪声并保持边界锐利性,结合交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)优化目标函数,显著提升了重建精度与鲁棒性。实验表明,改进算法在噪声环境或数据缺失场景下,能有效消除伪影并保留细节,重建质量优于传统方法。展开更多
空间变化PSF(Space-variant Point Spread Function,SVPSF)图像,即物空间各点的退化随位置的改变而改变的图像,由于其复原技术涉及到多个甚至海量PSF的提取、存储和运算,相对于空间不变PSF(Space-Invariant Point Spread Function,SIPSF...空间变化PSF(Space-variant Point Spread Function,SVPSF)图像,即物空间各点的退化随位置的改变而改变的图像,由于其复原技术涉及到多个甚至海量PSF的提取、存储和运算,相对于空间不变PSF(Space-Invariant Point Spread Function,SIPSF)图像复原要困难得多。目前处理此类图像的主要方法包括空间坐标转换法,等晕区分块复原法,以减少数据存储量,降低计算量,提高收敛速度为目标的直接复原法等。本文回顾了这一课题的研究历史,对目前的研究工作进行了分析和总结,介绍了本实验室提出的结合GRM(Gradient Ringing Metric)评价算法的总变分最小化图像分块复原法,并提出了未来工作关注重点的展望。展开更多
基金the National Natural Science Foundation of China(Nos.11401318 and 11671004)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.15KJB110018)the Scientific Research Foundation of NUPT(No.NY214023)
文摘Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image from its finite number of noisy compressive samples. A novel self-adaption, texture preservation method is designed to select the weight. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. The numerical examples are performed to compare its performance with four state-of-the-art algorithms. Experimental results clearly show that our method has better reconstruction accuracy on texture images than the existing scheme.
基金suppprt from NSFC of China,Singapore NTU project SUG 20/07,MOE Grant T207B2202NRF2007IDMIDM002-010
文摘In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 0. The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models. In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models. For images with strong noises, the restored images of the compound algorithm are the best in the corresponding restored images. The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration. It is found that the combination of these methods is efficient and robust in the image restoration.
基金supported by the National Key Research and Development Program of China(No.2022YFB1902700)the Joint Fund of Ministry of Education for Equipment Pre-research(No.8091B042203)+5 种基金the National Natural Science Foundation of China(No.11875129)the Fund of the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect(No.SKLIPR1810)the Fund of Innovation Center of Radiation Application(No.KFZC2020020402)the Fund of the State Key Laboratory of Nuclear Physics and Technology,Peking University(No.NPT2023KFY06)the Joint Innovation Fund of China National Uranium Co.,Ltd.,State Key Laboratory of Nuclear Resources and Environment,East China University of Technology(No.2022NRE-LH-02)the Fundamental Research Funds for the Central Universities(No.2023JG001).
文摘Neutron computed tomography(NCT)is widely used as a noninvasive measurement technique in nuclear engineering,thermal hydraulics,and cultural heritage.The neutron source intensity of NCT is usually low and the scan time is long,resulting in a projection image containing severe noise.To reduce the scanning time and increase the image reconstruction quality,an effective reconstruction algorithm must be selected.In CT image reconstruction,the reconstruction algorithms can be divided into three categories:analytical algorithms,iterative algorithms,and deep learning.Because the analytical algorithm requires complete projection data,it is not suitable for reconstruction in harsh environments,such as strong radia-tion,high temperature,and high pressure.Deep learning requires large amounts of data and complex models,which cannot be easily deployed,as well as has a high computational complexity and poor interpretability.Therefore,this paper proposes the OS-SART-PDTV iterative algorithm,which uses the ordered subset simultaneous algebraic reconstruction technique(OS-SART)algorithm to reconstruct the image and the first-order primal–dual algorithm to solve the total variation(PDTV),for sparse-view NCT three-dimensional reconstruction.The novel algorithm was compared with other algorithms(FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV)by simulating the experimental data and actual neutron projection experiments.The reconstruction results demonstrate that the proposed algorithm outperforms the FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV algorithms in terms of preserving edge structure,denoising,and suppressing artifacts.
基金This research is supported in part by RGC 7046/03P,7035/04P,7035/05P and HKBU FRGs.
文摘In[3],Chan and Wong proposed to use total variational regularization for both images and point spread functions in blind deconvolution.Their experimental results show that the detail of the restored images cannot be recovered.In this paper,we consider images in Lipschitz spaces,and propose to use Lipschitz regularization for images and total variational regularization for point spread functions in blind deconvolution.Our experimental results show that such combination of Lipschitz and total variational regularization methods can recover both images and point spread functions quite well.
基金supported by the Natural Science Foundation of China(Grant Nos.61971234,11501301,and 62001167)the“1311 Talent Plan”of NUPT,the“QingLan”Project for Colleges and Universities of Jiangsu Province,East China Normal University through startup funding,and Technology Innovation Training Program(Grant No.SZDG2019030).
文摘Image segmentation is a significant problem in image processing.In this paper,we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation(FBTV).The first stage of our method is to calculate a smooth solution from the FBTV Mumford-Shah model.Furthermore,we design a new difference of convex algorithm(DCA)with the semi-proximal alternating direction method of multipliers(sPADMM)iteration.In the second stage,we make use of the smooth solution and the K-means method to obtain the segmentation result.To simulate images more accurately,a useful operator is introduced,which enables the proposed model to segment not only the noisy or blurry images but the images with missing pixels well.Experiments demonstrate the proposed method produces more preferable results comparing with some state-of-the-art methods,especially on the images with missing pixels.
文摘Color image segmentation is crucial in image processing and computer vision.Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels,which contain some key feature information of the image.To better describe the relationship of color channels,we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively.Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation.The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space.Moreover,to accelerate the optimization process,we use a new primal-dual algorithm to solve our novel model.Numerical results demonstrate clearly that the performance of our proposed method is excellent.
文摘三维点云数据建模是三维重建的核心技术,广泛应用于医学、工业检测及文化遗产保护等领域。文章系统梳理了点云数据的结构与获取方法(如激光扫描、多视图成像),重点分析了基于隐式表示法的表面重建算法。该类算法通过隐式函数描述三维表面,擅长处理复杂的拓扑结构,但在面对噪声、稀疏数据及复杂场景时存在局限。为解决这些问题,文章提出了一种基于总变差(Total Variation,TV)正则化的改进算法,利用TV项抑制噪声并保持边界锐利性,结合交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)优化目标函数,显著提升了重建精度与鲁棒性。实验表明,改进算法在噪声环境或数据缺失场景下,能有效消除伪影并保留细节,重建质量优于传统方法。
文摘空间变化PSF(Space-variant Point Spread Function,SVPSF)图像,即物空间各点的退化随位置的改变而改变的图像,由于其复原技术涉及到多个甚至海量PSF的提取、存储和运算,相对于空间不变PSF(Space-Invariant Point Spread Function,SIPSF)图像复原要困难得多。目前处理此类图像的主要方法包括空间坐标转换法,等晕区分块复原法,以减少数据存储量,降低计算量,提高收敛速度为目标的直接复原法等。本文回顾了这一课题的研究历史,对目前的研究工作进行了分析和总结,介绍了本实验室提出的结合GRM(Gradient Ringing Metric)评价算法的总变分最小化图像分块复原法,并提出了未来工作关注重点的展望。
