Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5301230
Jian Liu, Xin Zhang, Kai Li
Nowadays, there are increasing amount of data fields of tide and tidal current gained from ocean dynamical environment real-time stereo monitoring platform, model calculation and numerical simulation. Collaborative visualization of these data fields on the internet is becoming increasingly important for the utility of data. In this paper, we summarize the characters of tide and tidal current data fields and introduce a data process and visualization system. We organize the original data follow a certain criterion, preprocess the data to improve the visualization effective, use snap-shot spatio-temporal data model to organize the data fields and display the changing process of tide and tidal current. Through a well-organized metadata database, data fields are well managed. By using web services we provide proper services to meet the needs of both the public and professionals on the internet.
{"title":"Study on Data Process and Visualization System of Tide and Tidal Current","authors":"Jian Liu, Xin Zhang, Kai Li","doi":"10.1109/CISP.2009.5301230","DOIUrl":"https://doi.org/10.1109/CISP.2009.5301230","url":null,"abstract":"Nowadays, there are increasing amount of data fields of tide and tidal current gained from ocean dynamical environment real-time stereo monitoring platform, model calculation and numerical simulation. Collaborative visualization of these data fields on the internet is becoming increasingly important for the utility of data. In this paper, we summarize the characters of tide and tidal current data fields and introduce a data process and visualization system. We organize the original data follow a certain criterion, preprocess the data to improve the visualization effective, use snap-shot spatio-temporal data model to organize the data fields and display the changing process of tide and tidal current. Through a well-organized metadata database, data fields are well managed. By using web services we provide proper services to meet the needs of both the public and professionals on the internet.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125539834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5303675
Jichao Jiao, Baojun Zhao, Jianke Li, Linbo Tang
In order to align the astronomical images with the characteristics of serious noise and smoothing edges, we propose an astronomical image registration based on the nonsubsampled contourlet transform (NSCT) and a new evaluation criterion to estimate the results of the registration. The registration algorithm includes coarse registration and fine registration. According to the shift-invariance of the NSCT, the approximate translations, which will be used to create the search windows of the fine registration, are obtained. Next, the local searches are operated in subband images, and then the feature points, which are extracted by using NSCT coefficients, are matched by utility of the gray correlation, and finally we can calculate the transformation parameters. The preliminary experimental results demonstrate the robustness and efficiency of the proposed algorithm in the noise suppression and the high registration accuracy which can achieve 0.2 pixels. Feature-based registration methods extract the edge features in the reference and sensed images, and then the parameters of the transform equation are obtained using these feature points, which are extracted by utilizing spatial relationships or similarity methods. The techniques of extracting the image edge features by using the edge detection operator or wavelet transform are proposed in the literature (4) (8), but the smooth edges cannot be effectively extracted. The algorithm based on the graph matching is proposed in the literature (9) and the random sample consensus (RANSAC) method is proposed in the literature (10), both of them are used to solve the optimal solution of the matching points. According to the characteristic of the astronomical images, whose change are slow and noise is relatively serious and feature structure is smoothing, a new astronomical images registration algorithm based on NSCT is proposed in this paper.
{"title":"A Multi-Stage Astronomical Images Registration Based on Nonsubsampled Contourlet Transform","authors":"Jichao Jiao, Baojun Zhao, Jianke Li, Linbo Tang","doi":"10.1109/CISP.2009.5303675","DOIUrl":"https://doi.org/10.1109/CISP.2009.5303675","url":null,"abstract":"In order to align the astronomical images with the characteristics of serious noise and smoothing edges, we propose an astronomical image registration based on the nonsubsampled contourlet transform (NSCT) and a new evaluation criterion to estimate the results of the registration. The registration algorithm includes coarse registration and fine registration. According to the shift-invariance of the NSCT, the approximate translations, which will be used to create the search windows of the fine registration, are obtained. Next, the local searches are operated in subband images, and then the feature points, which are extracted by using NSCT coefficients, are matched by utility of the gray correlation, and finally we can calculate the transformation parameters. The preliminary experimental results demonstrate the robustness and efficiency of the proposed algorithm in the noise suppression and the high registration accuracy which can achieve 0.2 pixels. Feature-based registration methods extract the edge features in the reference and sensed images, and then the parameters of the transform equation are obtained using these feature points, which are extracted by utilizing spatial relationships or similarity methods. The techniques of extracting the image edge features by using the edge detection operator or wavelet transform are proposed in the literature (4) (8), but the smooth edges cannot be effectively extracted. The algorithm based on the graph matching is proposed in the literature (9) and the random sample consensus (RANSAC) method is proposed in the literature (10), both of them are used to solve the optimal solution of the matching points. According to the characteristic of the astronomical images, whose change are slow and noise is relatively serious and feature structure is smoothing, a new astronomical images registration algorithm based on NSCT is proposed in this paper.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115037922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5304471
D. Mitrea, S. Nedevschi, M. Lupsor, M. Socaciu, R. Badea
The diagnosis of the malignant tumors is one of the major issues in nowadays research. We aim to elaborate a computerized, non-invasive method, for detecting the Hepatocellular Carcinoma (HCC), based on information from ultrasound images. For performing automatic detection of HCC, we elaborated the imagistic textural model of this malignant tumor, consisting in the relevant textural features and in their specific values for HCC. In this paper, we enhance the imagistic textural model of HCC, by using dimensionality reduction methods, the final purpose being that of obtaining an improvement of the classification process. Principal Component Analysis is a well known dimensionality reduction method, which maps the data into a new space, lower in dimension by finding the principal directions of variation. We experiment this method, studying its influence on the automatic diagnosis accuracy and we also try to combine it with Correlation based Feature Selection, for adding class label sensitivity.
{"title":"Improving the Textural Model of the Hepatocellular Carcinoma Using Dimensionality Reduction Methods","authors":"D. Mitrea, S. Nedevschi, M. Lupsor, M. Socaciu, R. Badea","doi":"10.1109/CISP.2009.5304471","DOIUrl":"https://doi.org/10.1109/CISP.2009.5304471","url":null,"abstract":"The diagnosis of the malignant tumors is one of the major issues in nowadays research. We aim to elaborate a computerized, non-invasive method, for detecting the Hepatocellular Carcinoma (HCC), based on information from ultrasound images. For performing automatic detection of HCC, we elaborated the imagistic textural model of this malignant tumor, consisting in the relevant textural features and in their specific values for HCC. In this paper, we enhance the imagistic textural model of HCC, by using dimensionality reduction methods, the final purpose being that of obtaining an improvement of the classification process. Principal Component Analysis is a well known dimensionality reduction method, which maps the data into a new space, lower in dimension by finding the principal directions of variation. We experiment this method, studying its influence on the automatic diagnosis accuracy and we also try to combine it with Correlation based Feature Selection, for adding class label sensitivity.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115081125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5303961
Xiu-kai Ruan, Zhi-Yong Zhang
Blind nonlinear channel equalization using kernel processing is proposed, which transforms blind equalization of nonlinear channel to formulate as a convex quadratic programming using kernel processing. The novel method acquires the optimal solution by solving a set of linear equations instead of solving a convex quadratic programming problem. It is shown the kernel processing equalization by adopting Gaussian cost function has several merit, such as: 1) The quadratic programming problem solved at each iteration is convex and has a globally optimal solution. 2) It avoids the difficulty of choosing the suitable parameters of the kernel function to obtain the satisfied blind equalization performance. 3) It need only 20% data samples of support vector machines (SVM) method to obtain the same blind equalization performance. 4) It is more robust for more nonlinear channels.
{"title":"Blind Nonlinear Channel Equalization Using Kernel Processing","authors":"Xiu-kai Ruan, Zhi-Yong Zhang","doi":"10.1109/CISP.2009.5303961","DOIUrl":"https://doi.org/10.1109/CISP.2009.5303961","url":null,"abstract":"Blind nonlinear channel equalization using kernel processing is proposed, which transforms blind equalization of nonlinear channel to formulate as a convex quadratic programming using kernel processing. The novel method acquires the optimal solution by solving a set of linear equations instead of solving a convex quadratic programming problem. It is shown the kernel processing equalization by adopting Gaussian cost function has several merit, such as: 1) The quadratic programming problem solved at each iteration is convex and has a globally optimal solution. 2) It avoids the difficulty of choosing the suitable parameters of the kernel function to obtain the satisfied blind equalization performance. 3) It need only 20% data samples of support vector machines (SVM) method to obtain the same blind equalization performance. 4) It is more robust for more nonlinear channels.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115324175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5301121
Jun Cai, F. Ran, Hui Yang, Meihua Xu
The desire characters for image sensor are high dynamic range and high capture speed. CMOS image sensors benefit from technology scaling by reducing pixel size, increasing resolution. The ability to reproduce a high-quality image depends strongly on the image sensor light sensitivity. Pixel design is a key part of image sensor design. A sensor with higher dynamic can detect a wider range of scene illumination and can produce images with greater detail. This paper discusses the design, layout and simulation results of a high dynamic range CMOS activepixel. Each pixel block consists of a phototransistor and 14 MOS transistors. Using self-reset, the scheme can extend dynamic range at the high end. The image data output is generated from the ADC output and the reset counter output. The sensor is designed with a 0.35μm CMOS technology and the fill-fact is 30%. Keywords-CMOS; image sensor; dynamic range; signal-tonoise ratio
{"title":"A CMOS Image Sensor with Self-Reset Circuit in Active Pixel","authors":"Jun Cai, F. Ran, Hui Yang, Meihua Xu","doi":"10.1109/CISP.2009.5301121","DOIUrl":"https://doi.org/10.1109/CISP.2009.5301121","url":null,"abstract":"The desire characters for image sensor are high dynamic range and high capture speed. CMOS image sensors benefit from technology scaling by reducing pixel size, increasing resolution. The ability to reproduce a high-quality image depends strongly on the image sensor light sensitivity. Pixel design is a key part of image sensor design. A sensor with higher dynamic can detect a wider range of scene illumination and can produce images with greater detail. This paper discusses the design, layout and simulation results of a high dynamic range CMOS activepixel. Each pixel block consists of a phototransistor and 14 MOS transistors. Using self-reset, the scheme can extend dynamic range at the high end. The image data output is generated from the ADC output and the reset counter output. The sensor is designed with a 0.35μm CMOS technology and the fill-fact is 30%. Keywords-CMOS; image sensor; dynamic range; signal-tonoise ratio","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116383653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5301038
Sheng Zhou, Xiaochun Wang, Yanqun Wang, Jun Yang
Objective To develop a signal processing system based on FPGA used in digital ophthalmology ultrasongraphy to process echo signals. Methods Main signal processing technique used in the system includes interpolation, dynamic filter, TGC, envelop demodulation and logarithmic amplifier. In FPGA, a schematic file acts as the top module, and hardware describe language VHDL act as bottom modules. Results By building a physical simulation model, the validity of each phases in signal processing of the system has been validated. By detecting normal human eyes and orbits, the echo signals gained are all right. Conclusion The system has good ability to process high frequency ultrasonic echo signals of ophthalmology in real time. It has reached design demands, and shows a good application prospect.
{"title":"A Signal Processing System Used in Digital Ophthalmology Ultrasonography","authors":"Sheng Zhou, Xiaochun Wang, Yanqun Wang, Jun Yang","doi":"10.1109/CISP.2009.5301038","DOIUrl":"https://doi.org/10.1109/CISP.2009.5301038","url":null,"abstract":"Objective To develop a signal processing system based on FPGA used in digital ophthalmology ultrasongraphy to process echo signals. Methods Main signal processing technique used in the system includes interpolation, dynamic filter, TGC, envelop demodulation and logarithmic amplifier. In FPGA, a schematic file acts as the top module, and hardware describe language VHDL act as bottom modules. Results By building a physical simulation model, the validity of each phases in signal processing of the system has been validated. By detecting normal human eyes and orbits, the echo signals gained are all right. Conclusion The system has good ability to process high frequency ultrasonic echo signals of ophthalmology in real time. It has reached design demands, and shows a good application prospect.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122592842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5305817
He Zhang, Lei Wan, Yu-shan Sun
Due to negative effects of underwater imaging environment and the real-time need of underwater task, a new underwater target recognition system is proposed. New combined invariant moments of underwater images are extracted as the system's recognition features,and the system's underwater target classifier is based on neural network which improved by Artificial Fish Swarm Algorithm (AFSA). AFSA is capable of attaining global optimum which can make up drawbacks of traditional BP neural network, such as converging slowly and tending to get into the local optimum. The proposed recognition system has been tested using four different kinds of targets images and disturbed images, targets' affine invariant features are extracted as the inputs of trained neural network and outputs of network are target classification. Experimental results show that the new system is well-clustering and with high classified accuracy. Keywords-underwater image; target recognition; moment invariant; neural network; artificial fish-swarm algorithm (AFSA)
{"title":"A New Approach to Underwater Target Recognition","authors":"He Zhang, Lei Wan, Yu-shan Sun","doi":"10.1109/CISP.2009.5305817","DOIUrl":"https://doi.org/10.1109/CISP.2009.5305817","url":null,"abstract":"Due to negative effects of underwater imaging environment and the real-time need of underwater task, a new underwater target recognition system is proposed. New combined invariant moments of underwater images are extracted as the system's recognition features,and the system's underwater target classifier is based on neural network which improved by Artificial Fish Swarm Algorithm (AFSA). AFSA is capable of attaining global optimum which can make up drawbacks of traditional BP neural network, such as converging slowly and tending to get into the local optimum. The proposed recognition system has been tested using four different kinds of targets images and disturbed images, targets' affine invariant features are extracted as the inputs of trained neural network and outputs of network are target classification. Experimental results show that the new system is well-clustering and with high classified accuracy. Keywords-underwater image; target recognition; moment invariant; neural network; artificial fish-swarm algorithm (AFSA)","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122610070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5304348
Hongsheng Yin, Peixi Zhang, Jian-sheng Qian, Gang Hua
Ventilator vibration signal is usually mixed with some signals and shows strong nonlinearity, nonstationarity and non- Gaussian. It presents a great challenge to feature extraction and recognition. We applied the independent component analysis (ICA) to ventilator vibration signal analysis, used FastICA algorithm to get a group of independent variables with the useful feature information, adopted residual self-information (RSI) to compress further for the group of independent variables, and chose the larger RSI to form the new estimating component. And then we used support vector machine (SVM) to find the ventilator healthy pattern and/or the ventilator fault pattern. The experiment result shows that by using the methods above the correct identification rate of ventilator healthy and fault state reaches 100%.
{"title":"Feature Extraction and Recognition of Ventilator Vibration Signal Based on ICA/SVM","authors":"Hongsheng Yin, Peixi Zhang, Jian-sheng Qian, Gang Hua","doi":"10.1109/CISP.2009.5304348","DOIUrl":"https://doi.org/10.1109/CISP.2009.5304348","url":null,"abstract":"Ventilator vibration signal is usually mixed with some signals and shows strong nonlinearity, nonstationarity and non- Gaussian. It presents a great challenge to feature extraction and recognition. We applied the independent component analysis (ICA) to ventilator vibration signal analysis, used FastICA algorithm to get a group of independent variables with the useful feature information, adopted residual self-information (RSI) to compress further for the group of independent variables, and chose the larger RSI to form the new estimating component. And then we used support vector machine (SVM) to find the ventilator healthy pattern and/or the ventilator fault pattern. The experiment result shows that by using the methods above the correct identification rate of ventilator healthy and fault state reaches 100%.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122963322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5303553
Wei Wang, Aidong Men, Xiaobo Chen
In this paper, a robust digital image watermarking scheme based on phase features in DFT domain and generalized Radon transformations is proposed. Within the framework of zero watermarking, the scheme selects the phase information in DFT domain as feature of image, and uses generalized Radon transformations to indentify the geometric transformations. From the experimental results, it shows that the proposed scheme is robust not only to common image processing, but also to the geometric transformations. Due to the robustness of the selected phase feature from DFT domain, the scheme is also robust to locally geometric transformations.
{"title":"Robust Image Watermarking Scheme Based on Phase Features in DFT Domain and Generalized Radon Transformations","authors":"Wei Wang, Aidong Men, Xiaobo Chen","doi":"10.1109/CISP.2009.5303553","DOIUrl":"https://doi.org/10.1109/CISP.2009.5303553","url":null,"abstract":"In this paper, a robust digital image watermarking scheme based on phase features in DFT domain and generalized Radon transformations is proposed. Within the framework of zero watermarking, the scheme selects the phase information in DFT domain as feature of image, and uses generalized Radon transformations to indentify the geometric transformations. From the experimental results, it shows that the proposed scheme is robust not only to common image processing, but also to the geometric transformations. Due to the robustness of the selected phase feature from DFT domain, the scheme is also robust to locally geometric transformations.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114054804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5305799
Huiqian Du, Wenbo Mei
Recently, exponential B-spline functions have been demonstrated as a new bridge between discrete and continuous time signal. In this paper, we propose an imaging resizing algorithm which exploits the merits of exponential B-spline functions. The theoretical background is introduced, and the analysis and synthesis filter are deduced. The main advantage of the algorithm is that it maintains the high frequency components of image so that the resized image has better visual quality. In addition, it can be used to resize images by any factor. The experimental results show that the algorithm outperforms the standard methods.
{"title":"Image Resizing Using Exponential B-Spline Functions","authors":"Huiqian Du, Wenbo Mei","doi":"10.1109/CISP.2009.5305799","DOIUrl":"https://doi.org/10.1109/CISP.2009.5305799","url":null,"abstract":"Recently, exponential B-spline functions have been demonstrated as a new bridge between discrete and continuous time signal. In this paper, we propose an imaging resizing algorithm which exploits the merits of exponential B-spline functions. The theoretical background is introduced, and the analysis and synthesis filter are deduced. The main advantage of the algorithm is that it maintains the high frequency components of image so that the resized image has better visual quality. In addition, it can be used to resize images by any factor. The experimental results show that the algorithm outperforms the standard methods.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114390211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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