彩色纹理图像分割的困难在于纹理图像成分的描述及彩色图像层与层之间的耦合。为解决该问题,基于多通道全变差规则项可优化彩色图像层与层之间的耦合,非局部算子可以描述纹理图像特征的特点,提出了彩色纹理图像分割的非局部Mumford-Sha...彩色纹理图像分割的困难在于纹理图像成分的描述及彩色图像层与层之间的耦合。为解决该问题,基于多通道全变差规则项可优化彩色图像层与层之间的耦合,非局部算子可以描述纹理图像特征的特点,提出了彩色纹理图像分割的非局部Mumford-Shah多通道全变差变分模型。所提模型综合多通道全变差模型、非局部Mumford-Shah模型优点,并用二值标记函数划分区域。为了提高数值计算效率,对所提出模型设计了ADMM(alternating direction method of multipliers)优化算法。最后,通过数值实验对比以及定性与定量分析表明方法对于彩色纹理图像的分割取得较好结果。展开更多
鉴于暗原色先验算法能复原不同雾浓度和场景深度的图像,而基于非局部算子概念的NL-CTV(Non-Local Color Total Variation)模型能较好地保持图像边缘和纹理等特征,融合暗原色先验与NL-CTV模型,提出了一种新型单幅彩色图像去雾模型。通过...鉴于暗原色先验算法能复原不同雾浓度和场景深度的图像,而基于非局部算子概念的NL-CTV(Non-Local Color Total Variation)模型能较好地保持图像边缘和纹理等特征,融合暗原色先验与NL-CTV模型,提出了一种新型单幅彩色图像去雾模型。通过暗原色先验得到精确的大气光强度和大气传输函数,然后推导包含大气光强度和大气传输函数的非局部能量泛函,再通过引入辅助变量和Bregman迭代参数,为其设计相应的快速split Bregman算法来求解该模型。将该算法与He算法、暗原色先验和Retinex算法的实验结果进行分析比较,从而验证了该模型不论从视觉上,还是客观数据上都要优于其他两种算法。展开更多
基于暗原色先验理论的算法可以对不同场景下的雾天图像进行有效去雾,但是去雾后图像通常含有较多噪声.而非局部MTV模型(Non-Local Multi-Channel Total Varia-tion)可以用于彩色图像去噪,同时又具有良好的保持边缘作用,并且对含有纹理...基于暗原色先验理论的算法可以对不同场景下的雾天图像进行有效去雾,但是去雾后图像通常含有较多噪声.而非局部MTV模型(Non-Local Multi-Channel Total Varia-tion)可以用于彩色图像去噪,同时又具有良好的保持边缘作用,并且对含有纹理的彩色图像去噪后依然能保留原有的纹理特征.文中将这两种方法结合在一起,提出新的图像去雾算法,首先建立大气光值与大气传输函数相关的能量泛函(H-NL-MTV模型),然后利用交替方向乘子法引入辅助变量求解能量泛函,最后利用MATLAB软件进行仿真实验.仿真结果表明,该模型得到的图像清晰自然,图像边缘保持良好,纹理特征得到保留.展开更多
In recent years,image processing based on stochastic resonance(SR)has received more and more attention.In this paper,a new model combining dynamical saturating nonlinearity with regularized variational term for enhanc...In recent years,image processing based on stochastic resonance(SR)has received more and more attention.In this paper,a new model combining dynamical saturating nonlinearity with regularized variational term for enhancement of low contrast image is proposed.The regularized variational term can be setting to total variation(TV),second order total generalized variation(TGV)and non-local means(NLM)in order to gradually suppress noise in the process of solving the model.In addition,the new model is tested on a mass of gray-scale images from standard test image and low contrast indoor color images from Low-Light dataset(LOL).By comparing the new model and other traditional image enhancement models,the results demonstrate the enhanced image not only obtain good perceptual quality but also get more excellent value of evaluation index compared with some previous methods.展开更多
文摘彩色纹理图像分割的困难在于纹理图像成分的描述及彩色图像层与层之间的耦合。为解决该问题,基于多通道全变差规则项可优化彩色图像层与层之间的耦合,非局部算子可以描述纹理图像特征的特点,提出了彩色纹理图像分割的非局部Mumford-Shah多通道全变差变分模型。所提模型综合多通道全变差模型、非局部Mumford-Shah模型优点,并用二值标记函数划分区域。为了提高数值计算效率,对所提出模型设计了ADMM(alternating direction method of multipliers)优化算法。最后,通过数值实验对比以及定性与定量分析表明方法对于彩色纹理图像的分割取得较好结果。
文摘鉴于暗原色先验算法能复原不同雾浓度和场景深度的图像,而基于非局部算子概念的NL-CTV(Non-Local Color Total Variation)模型能较好地保持图像边缘和纹理等特征,融合暗原色先验与NL-CTV模型,提出了一种新型单幅彩色图像去雾模型。通过暗原色先验得到精确的大气光强度和大气传输函数,然后推导包含大气光强度和大气传输函数的非局部能量泛函,再通过引入辅助变量和Bregman迭代参数,为其设计相应的快速split Bregman算法来求解该模型。将该算法与He算法、暗原色先验和Retinex算法的实验结果进行分析比较,从而验证了该模型不论从视觉上,还是客观数据上都要优于其他两种算法。
文摘基于暗原色先验理论的算法可以对不同场景下的雾天图像进行有效去雾,但是去雾后图像通常含有较多噪声.而非局部MTV模型(Non-Local Multi-Channel Total Varia-tion)可以用于彩色图像去噪,同时又具有良好的保持边缘作用,并且对含有纹理的彩色图像去噪后依然能保留原有的纹理特征.文中将这两种方法结合在一起,提出新的图像去雾算法,首先建立大气光值与大气传输函数相关的能量泛函(H-NL-MTV模型),然后利用交替方向乘子法引入辅助变量求解能量泛函,最后利用MATLAB软件进行仿真实验.仿真结果表明,该模型得到的图像清晰自然,图像边缘保持良好,纹理特征得到保留.
基金supported by the National Natural Science Foundation of China under Grant Nos. 61501276,61772294 and 61973179the China Postdoctoral Science Foundation under Grant No. 2016M592139the Qingdao Postdoctoral Applied Research Project under Grant No. 2015120
文摘In recent years,image processing based on stochastic resonance(SR)has received more and more attention.In this paper,a new model combining dynamical saturating nonlinearity with regularized variational term for enhancement of low contrast image is proposed.The regularized variational term can be setting to total variation(TV),second order total generalized variation(TGV)and non-local means(NLM)in order to gradually suppress noise in the process of solving the model.In addition,the new model is tested on a mass of gray-scale images from standard test image and low contrast indoor color images from Low-Light dataset(LOL).By comparing the new model and other traditional image enhancement models,the results demonstrate the enhanced image not only obtain good perceptual quality but also get more excellent value of evaluation index compared with some previous methods.