Total variation （TV） is widely applied in image process-ing. The assumption of TV is that an image consists of piecewise constants, however, it suffers from the so-cal ed staircase effect. In order to reduce the sta...Total variation （TV） is widely applied in image process-ing. The assumption of TV is that an image consists of piecewise constants, however, it suffers from the so-cal ed staircase effect. In order to reduce the staircase effect and preserve the edges when textures of image are extracted, a new image decomposition model is proposed in this paper. The proposed model is based on the to-tal generalized variation method which involves and balances the higher order of the structure. We also derive a numerical algorithm based on a primal-dual formulation that can be effectively imple-mented. Numerical experiments show that the proposed method can achieve a better trade-off between noise removal and texture extraction, while avoiding the staircase effect efficiently.展开更多
The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical ap- plications. This paper is...The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical ap- plications. This paper is based on the dark channel prior principle and aims at the prior information absent blurred image degradation situation. A lot of improvements have been made to estimate the transmission map of blurred images. Since the dark channel prior principle can effectively restore the blurred image at the cost of a large amount of computation, the total variation （TV） and image morphology transform （specifically top-hat transform and bottom- hat transform） have been introduced into the improved method. Compared with original transmission map estimation methods, the proposed method features both simplicity and accuracy. The es- timated transmission map together with the element can restore the image. Simulation results show that this method could inhibit the ill-posed problem during image restoration, meanwhile it can greatly improve the image quality and definition.展开更多
在图像去噪处理过程中,为了保持图像的边缘及内部纹理信息,提出一种基于全变差改进的加权维纳滤波图像去噪模型。提出的模型利用加权项将维纳滤波与改进后的全变差模型相结合,通过构建新算子建立新的扩散模型使得图像每一个像素点的梯...在图像去噪处理过程中,为了保持图像的边缘及内部纹理信息,提出一种基于全变差改进的加权维纳滤波图像去噪模型。提出的模型利用加权项将维纳滤波与改进后的全变差模型相结合,通过构建新算子建立新的扩散模型使得图像每一个像素点的梯度信息可以自适应地选择去噪的最佳模式来平滑噪声图像,既能够在保护边缘的条件下预先处理高斯噪声,同时可以克服全变差模型的"阶梯效应"。结果表明,新模型不仅能够有效去除噪声,强化边缘还有效地保证了边缘结构的细节信息。在峰值信号噪声比测试中,该模型较之于传统线性滤波法的信噪比提高了20 d B左右,均方差也大幅降低,更具理想性。展开更多
基金supported by the National Natural Science Foundation of China(6127129461301229)+1 种基金the Doctoral Research Fund of Henan University of Science and Technology(0900170809001751)
文摘Total variation （TV） is widely applied in image process-ing. The assumption of TV is that an image consists of piecewise constants, however, it suffers from the so-cal ed staircase effect. In order to reduce the staircase effect and preserve the edges when textures of image are extracted, a new image decomposition model is proposed in this paper. The proposed model is based on the to-tal generalized variation method which involves and balances the higher order of the structure. We also derive a numerical algorithm based on a primal-dual formulation that can be effectively imple-mented. Numerical experiments show that the proposed method can achieve a better trade-off between noise removal and texture extraction, while avoiding the staircase effect efficiently.
基金supported by the National Natural Science Foundation of China(61301095)the Chinese University Scientific Fund(HEUCF130807)the Chinese Defense Advanced Research Program of Science and Technology(10J3.1.6)
文摘The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical ap- plications. This paper is based on the dark channel prior principle and aims at the prior information absent blurred image degradation situation. A lot of improvements have been made to estimate the transmission map of blurred images. Since the dark channel prior principle can effectively restore the blurred image at the cost of a large amount of computation, the total variation （TV） and image morphology transform （specifically top-hat transform and bottom- hat transform） have been introduced into the improved method. Compared with original transmission map estimation methods, the proposed method features both simplicity and accuracy. The es- timated transmission map together with the element can restore the image. Simulation results show that this method could inhibit the ill-posed problem during image restoration, meanwhile it can greatly improve the image quality and definition.
基金National Natural Science Foundation of China，the Postdoc-toral Science Foundation of China，National Sci-ence Fund for Distinguished Young Scholars of China (61025014) Recommended by Associate Editor ZHOU Jie
文摘在图像去噪处理过程中,为了保持图像的边缘及内部纹理信息,提出一种基于全变差改进的加权维纳滤波图像去噪模型。提出的模型利用加权项将维纳滤波与改进后的全变差模型相结合,通过构建新算子建立新的扩散模型使得图像每一个像素点的梯度信息可以自适应地选择去噪的最佳模式来平滑噪声图像,既能够在保护边缘的条件下预先处理高斯噪声,同时可以克服全变差模型的"阶梯效应"。结果表明,新模型不仅能够有效去除噪声,强化边缘还有效地保证了边缘结构的细节信息。在峰值信号噪声比测试中,该模型较之于传统线性滤波法的信噪比提高了20 d B左右,均方差也大幅降低,更具理想性。
