Chaomin Shen - Academia.edu (original) (raw)
Papers by Chaomin Shen
International Journal of Remote Sensing, 2013
With the increasing availability of multisource image data from Earth observation satellites, ima... more With the increasing availability of multisource image data from Earth observation satellites, image fusion, a technique that produces a single image which preserves major salient features from a set of different inputs, has become an important tool in the field of remote sensing since usually the complete information cannot be obtained by a single sensor. In this article, we develop a new pixel-based variational model for image fusion using gradient features. The basic assumption is that the fused image should have a gradient that is close to the most salient gradient in the multisource inputs. Meanwhile, we integrate the inputs with the average quadratic local dispersion measure for the purpose of uniform and natural perception. Furthermore, we introduce a split Bregman algorithm to implement the proposed functional more effectively. To verify the effect of the proposed method, we visually and quantitatively compare it with the conventional image fusion schemes, such as the Laplacian pyramid, morphological pyramid, and geometry-based enhancement fusion methods. The results demonstrate the effectiveness and stability of the proposed method in terms of the related fusion evaluation benchmarks. In particular, the computation efficiency of the proposed method compared with other variational methods also shows that our method is remarkable.
IEEE Transactions on Image Processing, 2006
Journal of the Operations Research Society of China, 2019
Generative adversarial networks (GANs) have shown impressive power in the field of machine learni... more Generative adversarial networks (GANs) have shown impressive power in the field of machine learning. Traditional GANs have focused on unsupervised learning tasks. In recent years, conditional GANs that can generate data with labels have been proposed in semi-supervised learning and have achieved better image quality than traditional GANs. Conditional GANs, however, generally only minimize the difference between marginal distributions of real and generated data, neglecting the difference with respect to each class of the data. To address this challenge, we propose the GAN with joint dis
Journal of Mathematical Imaging and Vision, 2010
In this paper, we propose a variational soft segmentation framework inspired by the level set for... more In this paper, we propose a variational soft segmentation framework inspired by the level set formulation of multiphase Chan-Vese model. We use soft membership functions valued in [0, 1] to replace the Heaviside functions of level sets (or characteristic functions) such that we get a representation of regions by soft membership functions which automatically satisfies the sum to one constraint. We give general formulas for arbitrary N-phase segmentation, in contrast to Chan-Vese's level set method only 2 m-phase are studied. To ensure smoothness on membership functions, both total variation (TV) regularization and H 1 regularization used as two choices for the definition of regularization term. TV regularization has geometric meaning which requires that the segmentation curve length as short as possible, while H 1 regularization has no explicit geometric meaning but is easier to implement with less parameters and has higher tolerance to noise. Fast numerical schemes are designed for both of the regularization methods. By changing the distance function, the proposed segmentation framework can be easily extended to the segmentation of other types of images. Numerical results on cartoon images, piecewise smooth images and texture images demonstrate that our methods are effective in multiphase image segmentation.
Computers & Geosciences, 2009
We propose a novel semi-automatic method for burn scar delineation from Landsat imagery using a m... more We propose a novel semi-automatic method for burn scar delineation from Landsat imagery using a modified Chan-Vese model. Burn scars appear reddish-brown in band 742 false-colour composite of Landsat 7 images. This property is used in our algorithm to delineate burn scars. Firstly, we visually choose sample pixels from the burn scar. From these pixels, a discrimination function for burn scars is determined by the principal component analysis and interval estimation. Then we define a modified Chan-Vese functional. The minimizer of the functional corresponds to the boundary of the burn scar. In order to minimize this functional, the corresponding contour evolution equation is given. We use the discrimination function to locate an initial contour that is near the boundary of the burn scar. The evolving curve then efficiently converges to the desired boundary. A Landsat image over Russia is used to examine our algorithm. The result shows that the algorithm is effective.
Journal of Electronic Imaging, 2013
ABSTRACT An affine registration algorithm for multidimensional point sets under the framework of ... more ABSTRACT An affine registration algorithm for multidimensional point sets under the framework of Lie group is proposed. This algorithm studies the affine registration between two data sets, and puts the expectation maximization-iterative closest point (EM-ICP) algorithm into the framework of Lie group, since all affine transformations form a Lie transformation group. The registration is carried out via minimizing an energy functional depending on elements of the affine transformation Lie group. The key point for applying the idea of Lie group is that, during the minimization via iteration, we must guarantee the next iteration step of the transformation is still an element in the same group, starting from an element in a Lie group. Our solution is utilizing the element of Lie algebra to represent that of Lie group near the identity via the exponential map, i.e., we use the first canonical coordinate representation of Lie group. Several comparative experiments between the proposed Lie-EM-ICP algorithm and the Lie-ICP algorithm are performed, showing that the proposed algorithm is more accurate and robust, especially in the presence of outliers. This algorithm can also be generalized to other registration problems in general, provided that desired transformations are within certain Lie group.
ABSTRACT In this paper we propose a two-stage algorithm for oil slick segmentation in synthetic a... more ABSTRACT In this paper we propose a two-stage algorithm for oil slick segmentation in synthetic aperture radar (SAR) images. In the first stage, we propose a new variational model to reduce speckles in non-textured SAR images. Applications to simulated and real SAR images show that the method is well balanced in the quality of the conventional criteria. Then, in the second stage, we use the fast Chan-Vese (CV) model and the level set method to segment the oil slick in the de-speckled SAR image. The additive operator splitting (AOS) scheme is used in the numerical implementation to improve computational efficiency. Experimental results show that our two-stage algorithm is effective for oil slick segmentation in SAR images.
ABSTRACT The Aubert-Aujol (AA) model is a variational method for multiplicative noise removal. In... more ABSTRACT The Aubert-Aujol (AA) model is a variational method for multiplicative noise removal. In this paper, we study some basic properties of the regularization parameter in the AA model. We develop a method for automatically choosing the regularization parameter in the multiplicative noise removal process. In particular, we employ spatially varying regularization parameters in the AA model in order to restore more texture details of the denoised image. Experimental results are presented to demonstrate that the spatially varying regularization parameters method can obtain better denoised images than the other tested multiplicative noise removal methods.
ABSTRACT We propose a new method for image mosaicking. In the first stage, two images are registr... more ABSTRACT We propose a new method for image mosaicking. In the first stage, two images are registrated. In the second stage, two images are mosiacked and the intensities of the overlapped area are blended seamlessly. For image registration, our automatic registration method is conducted by defining an energy functional and solving the energy minimization problem. Our method is also suitable for cloud-contaminated data. Principal component analysis (PCA) and geodesic active contour method are used to detect clouds. The blended image is obtained by combining the two images with a weight. The weight function is constructed by solving Laplace equation with Dirichlet boundary conditions. Experiments show the effectiveness of our method.
ABSTRACT In this paper we propose a new variational model for image denoising and segmentation of... more ABSTRACT In this paper we propose a new variational model for image denoising and segmentation of both gray and color images. This method is inspired by the complex Ginzburg–Landau model and the weighted bounded variation model. Compared with active contour methods, our new algorithm can detect non-closed edges as well as quadruple junctions, and the initialization is completely automatic. The existence of the minimizer for our energy functional is proved. Numerical results show the effectiveness of our proposed model in image denoising and segmentation.
We propose a method for denoising a point cloud by pulling every noise point to its supposed posi... more We propose a method for denoising a point cloud by pulling every noise point to its supposed position. In R2, suppose that the point cloud be all located on a presumed curve. However, some points are not on the curve due to noise. For every point, in a small neighborhood the presumed curve is approximated by an osculating circle. The
ABSTRACT In this paper a new speckle reduction method for multi-polarisation Synthetic Aperture R... more ABSTRACT In this paper a new speckle reduction method for multi-polarisation Synthetic Aperture Radar (SAR) is proposed by using a constrained-variational model. Variational method is a new technique for SAR speckle removal. In this paper, we generalize the variational method from single-polarisation SAR into multi-polarisation SAR. For a given multi-polarisation SAR, we could define an energy functional. The energy evolves as the original image changes. When the energy reaches its minimum, the corresponding image is regarded as the desired result. In each channel of the multi-polarisation SAR, the speckle follows a Gamma law with mean mu = 1 and variance sigma2 = 1/M for M-look SAR. This statistical information is used to construct the energy functional. Our energy is a regularization term, which is the integral for the norm of image gradient, with constraints coming from each channel. Then we use the variational method and heat flow method to obtain the minimizer of the energy. A three-intensity image (|HH|2, |HV|2 and |VV|2) is used to demonstrate our algorithm. Numerical experiment shows a promising result.
ABSTRACT Registration of 3D range data is to find the transformation that best maps one data set ... more ABSTRACT Registration of 3D range data is to find the transformation that best maps one data set to the other. In this paper, Lie group parametric representation is combined with the Expectation Maximization (EM) method to provide a unified framework. First, having a transformation fixed, the EM algorithm is introduced to find the correspondence between two data sets through correspondence probability, which covers the relationship of all points, instead of using exact correspondence such as the classical Iterative Closest Point (ICP) method. With this type of ststistical correspondence, we could deal with the presence of the degradations such as outliers and incomplete point sets. Second, having the updated correspondence fixed, and introducing Lie group parametric representation, the transformation is updated by minimizing a quadratic programming. Then, an alternative iterative strategy by the above two steps is used to approximate the desired correspondence and transformation. The comparative experiment between our Lie-EM-ICP algorithm and Lie-ICP algorithm using point cloud is presented. Our algorithm is demonstrated to be accurate and robust, especially in the presence of incomplete point sets and outliers.
2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
International Journal of Remote Sensing, 2013
With the increasing availability of multisource image data from Earth observation satellites, ima... more With the increasing availability of multisource image data from Earth observation satellites, image fusion, a technique that produces a single image which preserves major salient features from a set of different inputs, has become an important tool in the field of remote sensing since usually the complete information cannot be obtained by a single sensor. In this article, we develop a new pixel-based variational model for image fusion using gradient features. The basic assumption is that the fused image should have a gradient that is close to the most salient gradient in the multisource inputs. Meanwhile, we integrate the inputs with the average quadratic local dispersion measure for the purpose of uniform and natural perception. Furthermore, we introduce a split Bregman algorithm to implement the proposed functional more effectively. To verify the effect of the proposed method, we visually and quantitatively compare it with the conventional image fusion schemes, such as the Laplacian pyramid, morphological pyramid, and geometry-based enhancement fusion methods. The results demonstrate the effectiveness and stability of the proposed method in terms of the related fusion evaluation benchmarks. In particular, the computation efficiency of the proposed method compared with other variational methods also shows that our method is remarkable.
IEEE Transactions on Image Processing, 2006
Journal of the Operations Research Society of China, 2019
Generative adversarial networks (GANs) have shown impressive power in the field of machine learni... more Generative adversarial networks (GANs) have shown impressive power in the field of machine learning. Traditional GANs have focused on unsupervised learning tasks. In recent years, conditional GANs that can generate data with labels have been proposed in semi-supervised learning and have achieved better image quality than traditional GANs. Conditional GANs, however, generally only minimize the difference between marginal distributions of real and generated data, neglecting the difference with respect to each class of the data. To address this challenge, we propose the GAN with joint dis
Journal of Mathematical Imaging and Vision, 2010
In this paper, we propose a variational soft segmentation framework inspired by the level set for... more In this paper, we propose a variational soft segmentation framework inspired by the level set formulation of multiphase Chan-Vese model. We use soft membership functions valued in [0, 1] to replace the Heaviside functions of level sets (or characteristic functions) such that we get a representation of regions by soft membership functions which automatically satisfies the sum to one constraint. We give general formulas for arbitrary N-phase segmentation, in contrast to Chan-Vese's level set method only 2 m-phase are studied. To ensure smoothness on membership functions, both total variation (TV) regularization and H 1 regularization used as two choices for the definition of regularization term. TV regularization has geometric meaning which requires that the segmentation curve length as short as possible, while H 1 regularization has no explicit geometric meaning but is easier to implement with less parameters and has higher tolerance to noise. Fast numerical schemes are designed for both of the regularization methods. By changing the distance function, the proposed segmentation framework can be easily extended to the segmentation of other types of images. Numerical results on cartoon images, piecewise smooth images and texture images demonstrate that our methods are effective in multiphase image segmentation.
Computers & Geosciences, 2009
We propose a novel semi-automatic method for burn scar delineation from Landsat imagery using a m... more We propose a novel semi-automatic method for burn scar delineation from Landsat imagery using a modified Chan-Vese model. Burn scars appear reddish-brown in band 742 false-colour composite of Landsat 7 images. This property is used in our algorithm to delineate burn scars. Firstly, we visually choose sample pixels from the burn scar. From these pixels, a discrimination function for burn scars is determined by the principal component analysis and interval estimation. Then we define a modified Chan-Vese functional. The minimizer of the functional corresponds to the boundary of the burn scar. In order to minimize this functional, the corresponding contour evolution equation is given. We use the discrimination function to locate an initial contour that is near the boundary of the burn scar. The evolving curve then efficiently converges to the desired boundary. A Landsat image over Russia is used to examine our algorithm. The result shows that the algorithm is effective.
Journal of Electronic Imaging, 2013
ABSTRACT An affine registration algorithm for multidimensional point sets under the framework of ... more ABSTRACT An affine registration algorithm for multidimensional point sets under the framework of Lie group is proposed. This algorithm studies the affine registration between two data sets, and puts the expectation maximization-iterative closest point (EM-ICP) algorithm into the framework of Lie group, since all affine transformations form a Lie transformation group. The registration is carried out via minimizing an energy functional depending on elements of the affine transformation Lie group. The key point for applying the idea of Lie group is that, during the minimization via iteration, we must guarantee the next iteration step of the transformation is still an element in the same group, starting from an element in a Lie group. Our solution is utilizing the element of Lie algebra to represent that of Lie group near the identity via the exponential map, i.e., we use the first canonical coordinate representation of Lie group. Several comparative experiments between the proposed Lie-EM-ICP algorithm and the Lie-ICP algorithm are performed, showing that the proposed algorithm is more accurate and robust, especially in the presence of outliers. This algorithm can also be generalized to other registration problems in general, provided that desired transformations are within certain Lie group.
ABSTRACT In this paper we propose a two-stage algorithm for oil slick segmentation in synthetic a... more ABSTRACT In this paper we propose a two-stage algorithm for oil slick segmentation in synthetic aperture radar (SAR) images. In the first stage, we propose a new variational model to reduce speckles in non-textured SAR images. Applications to simulated and real SAR images show that the method is well balanced in the quality of the conventional criteria. Then, in the second stage, we use the fast Chan-Vese (CV) model and the level set method to segment the oil slick in the de-speckled SAR image. The additive operator splitting (AOS) scheme is used in the numerical implementation to improve computational efficiency. Experimental results show that our two-stage algorithm is effective for oil slick segmentation in SAR images.
ABSTRACT The Aubert-Aujol (AA) model is a variational method for multiplicative noise removal. In... more ABSTRACT The Aubert-Aujol (AA) model is a variational method for multiplicative noise removal. In this paper, we study some basic properties of the regularization parameter in the AA model. We develop a method for automatically choosing the regularization parameter in the multiplicative noise removal process. In particular, we employ spatially varying regularization parameters in the AA model in order to restore more texture details of the denoised image. Experimental results are presented to demonstrate that the spatially varying regularization parameters method can obtain better denoised images than the other tested multiplicative noise removal methods.
ABSTRACT We propose a new method for image mosaicking. In the first stage, two images are registr... more ABSTRACT We propose a new method for image mosaicking. In the first stage, two images are registrated. In the second stage, two images are mosiacked and the intensities of the overlapped area are blended seamlessly. For image registration, our automatic registration method is conducted by defining an energy functional and solving the energy minimization problem. Our method is also suitable for cloud-contaminated data. Principal component analysis (PCA) and geodesic active contour method are used to detect clouds. The blended image is obtained by combining the two images with a weight. The weight function is constructed by solving Laplace equation with Dirichlet boundary conditions. Experiments show the effectiveness of our method.
ABSTRACT In this paper we propose a new variational model for image denoising and segmentation of... more ABSTRACT In this paper we propose a new variational model for image denoising and segmentation of both gray and color images. This method is inspired by the complex Ginzburg–Landau model and the weighted bounded variation model. Compared with active contour methods, our new algorithm can detect non-closed edges as well as quadruple junctions, and the initialization is completely automatic. The existence of the minimizer for our energy functional is proved. Numerical results show the effectiveness of our proposed model in image denoising and segmentation.
We propose a method for denoising a point cloud by pulling every noise point to its supposed posi... more We propose a method for denoising a point cloud by pulling every noise point to its supposed position. In R2, suppose that the point cloud be all located on a presumed curve. However, some points are not on the curve due to noise. For every point, in a small neighborhood the presumed curve is approximated by an osculating circle. The
ABSTRACT In this paper a new speckle reduction method for multi-polarisation Synthetic Aperture R... more ABSTRACT In this paper a new speckle reduction method for multi-polarisation Synthetic Aperture Radar (SAR) is proposed by using a constrained-variational model. Variational method is a new technique for SAR speckle removal. In this paper, we generalize the variational method from single-polarisation SAR into multi-polarisation SAR. For a given multi-polarisation SAR, we could define an energy functional. The energy evolves as the original image changes. When the energy reaches its minimum, the corresponding image is regarded as the desired result. In each channel of the multi-polarisation SAR, the speckle follows a Gamma law with mean mu = 1 and variance sigma2 = 1/M for M-look SAR. This statistical information is used to construct the energy functional. Our energy is a regularization term, which is the integral for the norm of image gradient, with constraints coming from each channel. Then we use the variational method and heat flow method to obtain the minimizer of the energy. A three-intensity image (|HH|2, |HV|2 and |VV|2) is used to demonstrate our algorithm. Numerical experiment shows a promising result.
ABSTRACT Registration of 3D range data is to find the transformation that best maps one data set ... more ABSTRACT Registration of 3D range data is to find the transformation that best maps one data set to the other. In this paper, Lie group parametric representation is combined with the Expectation Maximization (EM) method to provide a unified framework. First, having a transformation fixed, the EM algorithm is introduced to find the correspondence between two data sets through correspondence probability, which covers the relationship of all points, instead of using exact correspondence such as the classical Iterative Closest Point (ICP) method. With this type of ststistical correspondence, we could deal with the presence of the degradations such as outliers and incomplete point sets. Second, having the updated correspondence fixed, and introducing Lie group parametric representation, the transformation is updated by minimizing a quadratic programming. Then, an alternative iterative strategy by the above two steps is used to approximate the desired correspondence and transformation. The comparative experiment between our Lie-EM-ICP algorithm and Lie-ICP algorithm using point cloud is presented. Our algorithm is demonstrated to be accurate and robust, especially in the presence of incomplete point sets and outliers.
2007 IEEE International Geoscience and Remote Sensing Symposium, 2007