Cuiyin Liu - Academia.edu (original) (raw)

Papers by Cuiyin Liu

ArXiv, 2019

The optimization inspired network can bridge convex optimization and neural networks in Compressi... more The optimization inspired network can bridge convex optimization and neural networks in Compressive Sensing (CS) reconstruction of natural image, like ISTA-Net+, which mapping optimization algorithm: iterative shrinkage-thresholding algorithm (ISTA) into network. However, measurement matrix and input initialization are still hand-crafted, and multi-channel feature map contain information at different frequencies, which is treated equally across channels, hindering the ability of CS reconstruction in optimization-inspired networks. In order to solve the above problems, we proposed MC-ISTA-Net

KSII Transactions on Internet and Information Systems, 2019

Image enhancement is a challenging problem in the field of image processing, especially low-light... more Image enhancement is a challenging problem in the field of image processing, especially low-light color images enhancement. This paper proposed a robust and comprehensive enhancement method based several points. First, the idea of bright channel is introduced to estimate the illumination map which is used to attain the enhancing result with Retinex model, and the color constancy is keep as well. Second, in order eliminate the illumination offsets wrongly estimated, morphological closing operation is used to modify the initial estimating illumination. Furthermore, in order to avoid fabricating edges, enlarged noises and over-smoothed visual features appearing in enhancing result, a multi-scale closing operation is used. At last, in order to avoiding the haloes and artifacts presented in enhancing result caused by gradient information lost in previous step, guided filtering is introduced to deal with previous result with guided image is initial bright channel. The proposed method can get good illumination map, and attain very effective enhancing results, including dark area is enhanced with more visual features, color natural and constancy, avoiding artifacts and over-enhanced, and eliminating Incorrect light offsets.

Publications of the Astronomical Society of the Pacific, 2017

As a dedicated solar radio interferometer, the MingantU SpEctral RadioHeliograph (MUSER) generate... more As a dedicated solar radio interferometer, the MingantU SpEctral RadioHeliograph (MUSER) generates massive observational data in the frequency range of 400 MHz-15 GHz. High-performance imaging forms a significantly important aspect of MUSER's massive data processing requirements. In this study, we implement a practical highperformance imaging pipeline for MUSER data processing. At first, the specifications of the MUSER are introduced and its imaging requirements are analyzed. Referring to the most commonly used radio astronomy software such as CASA and MIRIAD, we then implement a high-performance imaging pipeline based on the Graphics Processing Unit technology with respect to the current operational status of the MUSER. A series of critical algorithms and their pseudo codes, i.e., detection of the solar disk and sky brightness, automatic centering of the solar disk and estimation of the number of iterations for clean algorithms, are proposed in detail. The preliminary experimental results indicate that the proposed imaging approach significantly increases the processing performance of MUSER and generates images with high-quality, which can meet the requirements of the MUSER data processing.

Publications of the Astronomical Society of the Pacific, 2016

The volume of data generated by modern astronomical telescopes is extremely large and rapidly gro... more The volume of data generated by modern astronomical telescopes is extremely large and rapidly growing. However, current high-performance data processing architectures/frameworks are not well suited for astronomers because of their limitations and programming difficulties. In this paper, we therefore present OpenCluster, an open-source distributed computing framework to support rapidly developing high-performance processing pipelines of astronomical big data. We first detail the OpenCluster design principles and implementations and present the APIs facilitated by the framework. We then demonstrate a case in which OpenCluster is used to resolve complex data processing problems for developing a pipeline for the Mingantu Ultrawide Spectral Radioheliograph. Finally, we present our OpenCluster performance evaluation. Overall, OpenCluster provides not only high fault tolerance and simple programming interfaces, but also a flexible means of scaling up the number of interacting entities. OpenCluster thereby provides an easily integrated distributed computing framework for quickly developing a high-performance data processing system of astronomical telescopes and for significantly reducing software development expenses.

Third International Conference on Digital Image Processing (ICDIP 2011), 2011

ABSTRACT

Lecture Notes in Electrical Engineering, 2012

In this paper, we present a novel algorithm for fuzzy segmentation of infrared image data using f... more In this paper, we present a novel algorithm for fuzzy segmentation of infrared image data using fuzzy clustering. A conventional FCM assigns the data into group, where the data is nearest to the center of group. Although FCM is populated in image segmentation, it still has the following disadvantages: (1) a conventional FCM algorithm does not consider spatial information for clustering. (2) The algorithm is sensitive to noise. In this paper we present a fuzzy-means algorithm that incorporates spatial information and the prior probability of a pixel neighborhood into the membership function for clustering. The modified FCM has a great improvement for noisy image and infrared image segmentation.

Lecture Notes in Electrical Engineering, 2012

FCM is populated in image segmentation for its simplicity and easily realization. The classic FCM... more FCM is populated in image segmentation for its simplicity and easily realization. The classic FCM segmentation used only the gray value for segmentation, and is liable to stuck at local values, and the result is relied on cluster center of initial selection. In this paper, we present a Genetic fuzzy c-means (GFCMS) algorithm that incorporates spatial information for segmentation. The first improvement is to use the spatial information of pixel in FCM algorithm. The second improvement is to use the genetic algorithm for searching the global optimum. The results of the experiment validates that the algorithm has better adaptability and gets the correct global optimum.

Journal of Computers, 2012

FCM is used for image segmentation in some applications. It is based on a specific distance norm ... more FCM is used for image segmentation in some applications. It is based on a specific distance norm and does not use spatial information of the image, so it has some drawbacks. Various kinds of improvements have been developed to extend the adaptability, such as BFCM, SFCM and KFCM. These methods extend FCM from two aspects, one is replacing the Euclidean norm, and the other is considering the spatial information constraints for clustering. Kernel distance can improve the robustness for multi-distribution data sets. Spatial information can help eliminate the sensitivity to noises and outliers. In this paper, Gaussian kernel-based fuzzy c-means algorithm with spatial information (KSFCM) is proposed. KSFCM is more robust and adaptive. The experiment results show that KSFCM has the better performance.

IEICE Transactions on Information and Systems, 2013

In this paper, an efficient multi-modal medical image fusion approach is proposed based on local ... more In this paper, an efficient multi-modal medical image fusion approach is proposed based on local features contrast and bilateral sharpness criterion in nonsubsampled contourlet transform (NSCT) domain. Compared with other multiscale decomposition analysis tools, the nonsubsampled contourlet transform not only can eliminate the "blockeffect" and the "pseudo-effect", but also can represent the source image in multiple direction and capture the geometric structure of source image in transform domain. These advantages of NSCT can, when used in fusion algorithm, help to attain more visual information in fused image and improve the fusion quality. At the same time, in order to improve the robustness of fusion algorithm and to improve the quality of the fused image, two selection rules should be considered. Firstly, a new bilateral sharpness criterion is proposed to select the lowpass coefficient, which exploits both strength and phase coherence. Secondly, a modified SML (sum modified Laplacian) is introduced into the local contrast measurements, which is suitable for human vision system and can extract more useful detailed information from source images. Experimental results demonstrate that the proposed method performs better than the conventional fusion algorithm in terms of both visual quality and objective evaluation criteria.

Applied Mechanics and Materials, 2010

The non-linear diffusion techniques were proposed for overcome the linear diffusion defaults. The... more The non-linear diffusion techniques were proposed for overcome the linear diffusion defaults. The linear diffusion was a homogeneous diffusivity with a constant conductivity. In this diffusion process, the noise and the edges were smoothed in the image. In order to prevent the edge from being smoothed during the denoising, the nonlinear diffusion was proposed by Pereona and Malik. In this method, noise was smoothed Simultaneously with the edges blurred. In diffusion processes, the conductivity is dependent on the image local information. We analyzed the ineffectiveness of isotropic and extended the work into the tensor-based anisotropic diffusion. It would be desirable to rotate the flux towards the orientation of interesting features. We compare the difference of isotroic linear and non-linear anisotropic diffusivity, and considere how to design non-linear anisotropic conductivity based on the different requires of the image filtering.

Complexity, 2019

It is urgent to combine knowledge resources with manufacturing business processes to form a knowl... more It is urgent to combine knowledge resources with manufacturing business processes to form a knowledge service in the cloud mode, so as to provide intelligent support for business activities in product development process. The main challenge of knowledge resource service, however, is how to rapidly construct the complex resource service system and respond promptly to the changeable service requirements in the business process, which is similar to the software system modeling using a component in software engineering. This paper is concerned with an optimal composition framework (OCF) of knowledge resource service, including service decomposition, component encapsulation, and optimal composition. Firstly, the typical business processes are decomposed into the dynamic knowledge element (DKE), and all kinds of knowledge resources and service behaviors are encapsulated into the reusable resource service components (RSC). Then, a multicomponent optimal composition mathematical model is pr...

Scientific Programming, 2021

For image registration, feature detection and description are critical steps that identify the ke... more For image registration, feature detection and description are critical steps that identify the keypoints and describe them for the subsequent matching to estimate the geometric transformation parameters between two images. Recently, there has been a large increase in the research methods of detection operators and description operators, from traditional methods to deep learning methods. To solve the problem, that is, which operator is suitable for specific application problems under different imaging conditions, the paper systematically reviewed commonly used descriptors and detectors from artificial methods to deep learning methods, and the corresponding principle, analysis, and comparative experiments are given as well. We introduce the handcrafted detectors including FAST, BRISK, ORB, SURF, SIFT, and KAZE and the handcrafted descriptors including BRISK, FREAK, BRIEF, SURF, ORB, SIFT, KAZE. At the same time, we review detectors based on deep learning technology including DetNet, T...

Chinese Science Bulletin, 2017

ABSTRACT This study is to investigate a new representation of a partition of an image domain into... more ABSTRACT This study is to investigate a new representation of a partition of an image domain into a number of regions using a level set method derived from a statistical framework. The proposed model is composed of evolving simple closed planar curves by a region-based force determined by maximizing the posterior image densities over all possible partitions of the image plane containing three terms: a Bayesian term based on the prior probability, a regularity term adopted to avoid the generation of excessively irregular and small segmented regions, and a term based on a region merging prior related to region area, which is applied to allow the number of regions to vary automatically during curve evolution and therefore can optimize the objective functional implicitly with respect to the number of regions. This formulation leads to a system of coupled curve evolution equations, which is easily amenable to a level set implementation, and an unambiguous segmentation because the evolving regions form a partition of the image domain at all times during curve evolution. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results on both synthetic and real images.

Publications of the Astronomical Society of the Pacific, Jun 22, 2017

As a dedicated synthetic aperture radio interferometer, the MingantU SpEctral Radioheliograph (MU... more As a dedicated synthetic aperture radio interferometer, the MingantU SpEctral Radioheliograph (MUSER), initially known as the Chinese Spectral RadioHeliograph (CSRH), has entered the stage of routine observation. More than 23 million data records per day need to be effectively managed to provide high performance data query and retrieval for scientific data reduction. In light of these massive amounts of data generated by the MUSER, in this paper, a novel data management technique called the negative database (ND) is proposed and used to implement a data management system for the MUSER. Based on the key-value database, the ND technique makes complete utilization of the complement set of observational data to derive the requisite information. Experimental results showed that the proposed ND can significantly reduce storage volume in comparison with a relational database management system (RDBMS). Even when considering the time needed to derive records that were absent, its overall performance, including querying and deriving the data of the ND, is comparable with that of an RDBMS. The ND technique effectively solves the problem of massive data storage for the MUSER, and is a valuable reference for the massive data management required in next-generation telescopes.

ArXiv, 2019

The optimization inspired network can bridge convex optimization and neural networks in Compressi... more The optimization inspired network can bridge convex optimization and neural networks in Compressive Sensing (CS) reconstruction of natural image, like ISTA-Net+, which mapping optimization algorithm: iterative shrinkage-thresholding algorithm (ISTA) into network. However, measurement matrix and input initialization are still hand-crafted, and multi-channel feature map contain information at different frequencies, which is treated equally across channels, hindering the ability of CS reconstruction in optimization-inspired networks. In order to solve the above problems, we proposed MC-ISTA-Net

KSII Transactions on Internet and Information Systems, 2019

Image enhancement is a challenging problem in the field of image processing, especially low-light... more Image enhancement is a challenging problem in the field of image processing, especially low-light color images enhancement. This paper proposed a robust and comprehensive enhancement method based several points. First, the idea of bright channel is introduced to estimate the illumination map which is used to attain the enhancing result with Retinex model, and the color constancy is keep as well. Second, in order eliminate the illumination offsets wrongly estimated, morphological closing operation is used to modify the initial estimating illumination. Furthermore, in order to avoid fabricating edges, enlarged noises and over-smoothed visual features appearing in enhancing result, a multi-scale closing operation is used. At last, in order to avoiding the haloes and artifacts presented in enhancing result caused by gradient information lost in previous step, guided filtering is introduced to deal with previous result with guided image is initial bright channel. The proposed method can get good illumination map, and attain very effective enhancing results, including dark area is enhanced with more visual features, color natural and constancy, avoiding artifacts and over-enhanced, and eliminating Incorrect light offsets.

Publications of the Astronomical Society of the Pacific, 2017

As a dedicated solar radio interferometer, the MingantU SpEctral RadioHeliograph (MUSER) generate... more As a dedicated solar radio interferometer, the MingantU SpEctral RadioHeliograph (MUSER) generates massive observational data in the frequency range of 400 MHz-15 GHz. High-performance imaging forms a significantly important aspect of MUSER's massive data processing requirements. In this study, we implement a practical highperformance imaging pipeline for MUSER data processing. At first, the specifications of the MUSER are introduced and its imaging requirements are analyzed. Referring to the most commonly used radio astronomy software such as CASA and MIRIAD, we then implement a high-performance imaging pipeline based on the Graphics Processing Unit technology with respect to the current operational status of the MUSER. A series of critical algorithms and their pseudo codes, i.e., detection of the solar disk and sky brightness, automatic centering of the solar disk and estimation of the number of iterations for clean algorithms, are proposed in detail. The preliminary experimental results indicate that the proposed imaging approach significantly increases the processing performance of MUSER and generates images with high-quality, which can meet the requirements of the MUSER data processing.

Publications of the Astronomical Society of the Pacific, 2016

The volume of data generated by modern astronomical telescopes is extremely large and rapidly gro... more The volume of data generated by modern astronomical telescopes is extremely large and rapidly growing. However, current high-performance data processing architectures/frameworks are not well suited for astronomers because of their limitations and programming difficulties. In this paper, we therefore present OpenCluster, an open-source distributed computing framework to support rapidly developing high-performance processing pipelines of astronomical big data. We first detail the OpenCluster design principles and implementations and present the APIs facilitated by the framework. We then demonstrate a case in which OpenCluster is used to resolve complex data processing problems for developing a pipeline for the Mingantu Ultrawide Spectral Radioheliograph. Finally, we present our OpenCluster performance evaluation. Overall, OpenCluster provides not only high fault tolerance and simple programming interfaces, but also a flexible means of scaling up the number of interacting entities. OpenCluster thereby provides an easily integrated distributed computing framework for quickly developing a high-performance data processing system of astronomical telescopes and for significantly reducing software development expenses.

Third International Conference on Digital Image Processing (ICDIP 2011), 2011

ABSTRACT

Lecture Notes in Electrical Engineering, 2012

In this paper, we present a novel algorithm for fuzzy segmentation of infrared image data using f... more In this paper, we present a novel algorithm for fuzzy segmentation of infrared image data using fuzzy clustering. A conventional FCM assigns the data into group, where the data is nearest to the center of group. Although FCM is populated in image segmentation, it still has the following disadvantages: (1) a conventional FCM algorithm does not consider spatial information for clustering. (2) The algorithm is sensitive to noise. In this paper we present a fuzzy-means algorithm that incorporates spatial information and the prior probability of a pixel neighborhood into the membership function for clustering. The modified FCM has a great improvement for noisy image and infrared image segmentation.

Lecture Notes in Electrical Engineering, 2012

FCM is populated in image segmentation for its simplicity and easily realization. The classic FCM... more FCM is populated in image segmentation for its simplicity and easily realization. The classic FCM segmentation used only the gray value for segmentation, and is liable to stuck at local values, and the result is relied on cluster center of initial selection. In this paper, we present a Genetic fuzzy c-means (GFCMS) algorithm that incorporates spatial information for segmentation. The first improvement is to use the spatial information of pixel in FCM algorithm. The second improvement is to use the genetic algorithm for searching the global optimum. The results of the experiment validates that the algorithm has better adaptability and gets the correct global optimum.

Journal of Computers, 2012

FCM is used for image segmentation in some applications. It is based on a specific distance norm ... more FCM is used for image segmentation in some applications. It is based on a specific distance norm and does not use spatial information of the image, so it has some drawbacks. Various kinds of improvements have been developed to extend the adaptability, such as BFCM, SFCM and KFCM. These methods extend FCM from two aspects, one is replacing the Euclidean norm, and the other is considering the spatial information constraints for clustering. Kernel distance can improve the robustness for multi-distribution data sets. Spatial information can help eliminate the sensitivity to noises and outliers. In this paper, Gaussian kernel-based fuzzy c-means algorithm with spatial information (KSFCM) is proposed. KSFCM is more robust and adaptive. The experiment results show that KSFCM has the better performance.

IEICE Transactions on Information and Systems, 2013

In this paper, an efficient multi-modal medical image fusion approach is proposed based on local ... more In this paper, an efficient multi-modal medical image fusion approach is proposed based on local features contrast and bilateral sharpness criterion in nonsubsampled contourlet transform (NSCT) domain. Compared with other multiscale decomposition analysis tools, the nonsubsampled contourlet transform not only can eliminate the "blockeffect" and the "pseudo-effect", but also can represent the source image in multiple direction and capture the geometric structure of source image in transform domain. These advantages of NSCT can, when used in fusion algorithm, help to attain more visual information in fused image and improve the fusion quality. At the same time, in order to improve the robustness of fusion algorithm and to improve the quality of the fused image, two selection rules should be considered. Firstly, a new bilateral sharpness criterion is proposed to select the lowpass coefficient, which exploits both strength and phase coherence. Secondly, a modified SML (sum modified Laplacian) is introduced into the local contrast measurements, which is suitable for human vision system and can extract more useful detailed information from source images. Experimental results demonstrate that the proposed method performs better than the conventional fusion algorithm in terms of both visual quality and objective evaluation criteria.

Applied Mechanics and Materials, 2010

The non-linear diffusion techniques were proposed for overcome the linear diffusion defaults. The... more The non-linear diffusion techniques were proposed for overcome the linear diffusion defaults. The linear diffusion was a homogeneous diffusivity with a constant conductivity. In this diffusion process, the noise and the edges were smoothed in the image. In order to prevent the edge from being smoothed during the denoising, the nonlinear diffusion was proposed by Pereona and Malik. In this method, noise was smoothed Simultaneously with the edges blurred. In diffusion processes, the conductivity is dependent on the image local information. We analyzed the ineffectiveness of isotropic and extended the work into the tensor-based anisotropic diffusion. It would be desirable to rotate the flux towards the orientation of interesting features. We compare the difference of isotroic linear and non-linear anisotropic diffusivity, and considere how to design non-linear anisotropic conductivity based on the different requires of the image filtering.

Complexity, 2019

It is urgent to combine knowledge resources with manufacturing business processes to form a knowl... more It is urgent to combine knowledge resources with manufacturing business processes to form a knowledge service in the cloud mode, so as to provide intelligent support for business activities in product development process. The main challenge of knowledge resource service, however, is how to rapidly construct the complex resource service system and respond promptly to the changeable service requirements in the business process, which is similar to the software system modeling using a component in software engineering. This paper is concerned with an optimal composition framework (OCF) of knowledge resource service, including service decomposition, component encapsulation, and optimal composition. Firstly, the typical business processes are decomposed into the dynamic knowledge element (DKE), and all kinds of knowledge resources and service behaviors are encapsulated into the reusable resource service components (RSC). Then, a multicomponent optimal composition mathematical model is pr...

Scientific Programming, 2021

For image registration, feature detection and description are critical steps that identify the ke... more For image registration, feature detection and description are critical steps that identify the keypoints and describe them for the subsequent matching to estimate the geometric transformation parameters between two images. Recently, there has been a large increase in the research methods of detection operators and description operators, from traditional methods to deep learning methods. To solve the problem, that is, which operator is suitable for specific application problems under different imaging conditions, the paper systematically reviewed commonly used descriptors and detectors from artificial methods to deep learning methods, and the corresponding principle, analysis, and comparative experiments are given as well. We introduce the handcrafted detectors including FAST, BRISK, ORB, SURF, SIFT, and KAZE and the handcrafted descriptors including BRISK, FREAK, BRIEF, SURF, ORB, SIFT, KAZE. At the same time, we review detectors based on deep learning technology including DetNet, T...

Chinese Science Bulletin, 2017

ABSTRACT This study is to investigate a new representation of a partition of an image domain into... more ABSTRACT This study is to investigate a new representation of a partition of an image domain into a number of regions using a level set method derived from a statistical framework. The proposed model is composed of evolving simple closed planar curves by a region-based force determined by maximizing the posterior image densities over all possible partitions of the image plane containing three terms: a Bayesian term based on the prior probability, a regularity term adopted to avoid the generation of excessively irregular and small segmented regions, and a term based on a region merging prior related to region area, which is applied to allow the number of regions to vary automatically during curve evolution and therefore can optimize the objective functional implicitly with respect to the number of regions. This formulation leads to a system of coupled curve evolution equations, which is easily amenable to a level set implementation, and an unambiguous segmentation because the evolving regions form a partition of the image domain at all times during curve evolution. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results on both synthetic and real images.

Publications of the Astronomical Society of the Pacific, Jun 22, 2017

As a dedicated synthetic aperture radio interferometer, the MingantU SpEctral Radioheliograph (MU... more As a dedicated synthetic aperture radio interferometer, the MingantU SpEctral Radioheliograph (MUSER), initially known as the Chinese Spectral RadioHeliograph (CSRH), has entered the stage of routine observation. More than 23 million data records per day need to be effectively managed to provide high performance data query and retrieval for scientific data reduction. In light of these massive amounts of data generated by the MUSER, in this paper, a novel data management technique called the negative database (ND) is proposed and used to implement a data management system for the MUSER. Based on the key-value database, the ND technique makes complete utilization of the complement set of observational data to derive the requisite information. Experimental results showed that the proposed ND can significantly reduce storage volume in comparison with a relational database management system (RDBMS). Even when considering the time needed to derive records that were absent, its overall performance, including querying and deriving the data of the ND, is comparable with that of an RDBMS. The ND technique effectively solves the problem of massive data storage for the MUSER, and is a valuable reference for the massive data management required in next-generation telescopes.