The classical TV (Total Variation) model has been applied to gray texture image denoising and inpainting previously based on the non local operators, but such model can not be directly used to color texture image inpa...The classical TV (Total Variation) model has been applied to gray texture image denoising and inpainting previously based on the non local operators, but such model can not be directly used to color texture image inpainting due to coupling of different image layers in color images. In order to solve the inpainting problem for color texture images effectively, we propose a non local CTV (Color Total Variation) model. Technically, the proposed model is an extension of local TV model for gray images but we take account of the coupling of different layers in color images and make use of concepts of the non-local operators. As the coupling of different layers for color images in the proposed model will in-crease computational complexity, we also design a fast Split Bregman algorithm. Finally, some numerical experiments are conducted to validate the performance of the proposed model and its algorithm.展开更多
To solve the problem of false edges in a flat region of l_(1)norm total variational TV model,an edge extractor based on non-local idea is proposed in this paper.The new edge extractor can effectively suppress the infl...To solve the problem of false edges in a flat region of l_(1)norm total variational TV model,an edge extractor based on non-local idea is proposed in this paper.The new edge extractor can effectively suppress the influence of noise and extract the edge information of the image.The new edge extractor is used as the adaptive function and the weighting function of the l_(p) norm variational model to control the noise reduction ability of the model,and a new model 1 is obtained.Considering that the new model 1 only uses the gradient mode as the image feature operator,which is insufficient to express the image texture information,a new level set curvature gradient variational model 2 combined with the edge extractor is proposed.The new model 2 uses the idea of minimum curvature of the level set of clear images to obtain noise reduction images.By coupling new model 1 and new model 2 to smooth the noise and protect more textures,a new Non-local level set denoising model(NLSDM)for image noise reduction is obtained.The experimental results show that compared with the noise reduction model,the new model has significantly improved the peak signal-to-noise ratio and structural similarity,and the effect of noise reduction and edge preservation is better.展开更多
The traditional Total-Variation algorithm has a good result to de-noise for noise image of small scale details, but it easily losses the details for the image with rich texture and tiny boundary. In order to solve thi...The traditional Total-Variation algorithm has a good result to de-noise for noise image of small scale details, but it easily losses the details for the image with rich texture and tiny boundary. In order to solve this problem, this paper proposes a Sobel-TV model algorithm for image denoising. It uses TV model to de-noise and uses Sobel algorithm to control smoothness of image, which not only efficiently removes image noise but also simultaneously retail information, such as edge and texture. The experiments demonstrate that the proposed algorithm is simple, practical and generates better SNR, which is an important value to preprocess image.展开更多
基于局部算子不同形式的TV(total variation)模型用于彩色图像的噪声去除时往往存在边缘模糊、纹理模糊、阶梯效应、Mosaic效应等问题。因此,将传统局部的Tikhonov模型、TV模型、MTV(multi-channel total variation)模型、CTV(color tot...基于局部算子不同形式的TV(total variation)模型用于彩色图像的噪声去除时往往存在边缘模糊、纹理模糊、阶梯效应、Mosaic效应等问题。因此,将传统局部的Tikhonov模型、TV模型、MTV(multi-channel total variation)模型、CTV(color total variation)模型推广到基于非局部算子概念的NL-CT(non-local color Tikhonov)模型、NL-LTV(non-local layered total variation)模型、NL-MTV(non-local multi-channel total variation)模型、NL-CTV(non-local colortotal variation)模型,并通过引入辅助变量和Bregman迭代参数设计了相应的快速Split Bregman算法。实验结果表明,所提出的非局部TV模型都很好地解决了局部模型中出现的问题,在纹理、边缘、光滑度等特征保持方面取得了良好特性,其中NL-CTV处理效果最好,但是计算效率较低。展开更多
为解决Curvelet图像去噪所产生的"环绕"效应以及非局部TV模型去噪过度平滑而无法保持细小纹理的问题,本文提出了一种基于Curvelet变换与非局部TV模型相结合的图像去噪方法(Curvelet and Non-Local TV,CNL-TV)。该方法首先对...为解决Curvelet图像去噪所产生的"环绕"效应以及非局部TV模型去噪过度平滑而无法保持细小纹理的问题,本文提出了一种基于Curvelet变换与非局部TV模型相结合的图像去噪方法(Curvelet and Non-Local TV,CNL-TV)。该方法首先对含噪图像进行Curvelet变换,将其分解成不同尺度的图像;其次根据每层图像的特性,选择合适的非局部TV模型参数分别进行处理;最后将处理后的每层图像融合。实验结果表明,该算法不仅能够有效地减少噪声,消除Curvelet去噪产生的"环绕"效应,而且最大程度地保持了图像中的细小纹理成分。通过比较不同方法所得结果的峰值信噪比,验证了算法的有效性。展开更多
文摘The classical TV (Total Variation) model has been applied to gray texture image denoising and inpainting previously based on the non local operators, but such model can not be directly used to color texture image inpainting due to coupling of different image layers in color images. In order to solve the inpainting problem for color texture images effectively, we propose a non local CTV (Color Total Variation) model. Technically, the proposed model is an extension of local TV model for gray images but we take account of the coupling of different layers in color images and make use of concepts of the non-local operators. As the coupling of different layers for color images in the proposed model will in-crease computational complexity, we also design a fast Split Bregman algorithm. Finally, some numerical experiments are conducted to validate the performance of the proposed model and its algorithm.
基金funded by National Nature Science Foundation of China,grant number 61302188.
文摘To solve the problem of false edges in a flat region of l_(1)norm total variational TV model,an edge extractor based on non-local idea is proposed in this paper.The new edge extractor can effectively suppress the influence of noise and extract the edge information of the image.The new edge extractor is used as the adaptive function and the weighting function of the l_(p) norm variational model to control the noise reduction ability of the model,and a new model 1 is obtained.Considering that the new model 1 only uses the gradient mode as the image feature operator,which is insufficient to express the image texture information,a new level set curvature gradient variational model 2 combined with the edge extractor is proposed.The new model 2 uses the idea of minimum curvature of the level set of clear images to obtain noise reduction images.By coupling new model 1 and new model 2 to smooth the noise and protect more textures,a new Non-local level set denoising model(NLSDM)for image noise reduction is obtained.The experimental results show that compared with the noise reduction model,the new model has significantly improved the peak signal-to-noise ratio and structural similarity,and the effect of noise reduction and edge preservation is better.
文摘The traditional Total-Variation algorithm has a good result to de-noise for noise image of small scale details, but it easily losses the details for the image with rich texture and tiny boundary. In order to solve this problem, this paper proposes a Sobel-TV model algorithm for image denoising. It uses TV model to de-noise and uses Sobel algorithm to control smoothness of image, which not only efficiently removes image noise but also simultaneously retail information, such as edge and texture. The experiments demonstrate that the proposed algorithm is simple, practical and generates better SNR, which is an important value to preprocess image.
文摘基于局部算子不同形式的TV(total variation)模型用于彩色图像的噪声去除时往往存在边缘模糊、纹理模糊、阶梯效应、Mosaic效应等问题。因此,将传统局部的Tikhonov模型、TV模型、MTV(multi-channel total variation)模型、CTV(color total variation)模型推广到基于非局部算子概念的NL-CT(non-local color Tikhonov)模型、NL-LTV(non-local layered total variation)模型、NL-MTV(non-local multi-channel total variation)模型、NL-CTV(non-local colortotal variation)模型,并通过引入辅助变量和Bregman迭代参数设计了相应的快速Split Bregman算法。实验结果表明,所提出的非局部TV模型都很好地解决了局部模型中出现的问题,在纹理、边缘、光滑度等特征保持方面取得了良好特性,其中NL-CTV处理效果最好,但是计算效率较低。
文摘为解决Curvelet图像去噪所产生的"环绕"效应以及非局部TV模型去噪过度平滑而无法保持细小纹理的问题,本文提出了一种基于Curvelet变换与非局部TV模型相结合的图像去噪方法(Curvelet and Non-Local TV,CNL-TV)。该方法首先对含噪图像进行Curvelet变换,将其分解成不同尺度的图像;其次根据每层图像的特性,选择合适的非局部TV模型参数分别进行处理;最后将处理后的每层图像融合。实验结果表明,该算法不仅能够有效地减少噪声,消除Curvelet去噪产生的"环绕"效应,而且最大程度地保持了图像中的细小纹理成分。通过比较不同方法所得结果的峰值信噪比,验证了算法的有效性。
