Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for S...Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.展开更多
A multi dimensional concatenation scheme for block codes is introduced, in which information symbols are interleaved and re encoded for more than once. It provides a convenient platform to design high performance co...A multi dimensional concatenation scheme for block codes is introduced, in which information symbols are interleaved and re encoded for more than once. It provides a convenient platform to design high performance codes with flexible interleaver size. Coset based MAP soft in/soft out decoding algorithms are presented for the F24 code. Simulation results show that the proposed coding scheme can achieve high coding gain with flexible interleaver length and very low decoding complexity.展开更多
Synthetic aperture radar （SAR） imagery is a kind of coherent system that produces a random pattern, named speckle, which degrades the merit of SAR images and affects their further application seriously. Therefore, h...Synthetic aperture radar （SAR） imagery is a kind of coherent system that produces a random pattern, named speckle, which degrades the merit of SAR images and affects their further application seriously. Therefore, how to restore SAR image from the speckle has become a necessary step in post-processing of image. A new despeckling method is putforth on the basis of wavelet. First, a new approach on the basis of ＂second kind statistics＂ is used to estimate the dispersion parameter of the Cauchy distribution. Then, this Cauchy prior is applied to model the distribution of the wavelet coefficients for the log-transformed reflectance of SAR image. Based on the above ideas, a new homomorphic wavelet-based maximum a posterior （MAP） despeckling method is proposed. Finally, the simulated speckled image and the real SAR image are used to verify our proposed method and the results show that it outperforms the other methods in terms of the speckle reduction and the feature retention.展开更多
Construction of high resolution images from low resolution sequences having rigid or semi-rigid ob-jects with unified motions is often important in surveillance and other applications.In this paper a novelobject-based...Construction of high resolution images from low resolution sequences having rigid or semi-rigid ob-jects with unified motions is often important in surveillance and other applications.In this paper a novelobject-based super resolution reconstruction scheme was proposed,in which a six-parameter affine model-based object tracking and registration method was first used to segment and match objects among a se-quence of low resolution frames.The motion model was then further extended to the traditional maximuma posterior(MAP)super resolution algorithm.The proposed object tracking and registration method wasevaluated by both simulated and real acquired sequences.The results have demonstrated the high accura-cy of the proposed object based method and the enhanced reconstruction performance of the extended ap-proach.展开更多
This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first l...This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses 'Filters, Random and Maximum Entropy (Abb. FRAME)' model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.展开更多
基金supported by the Specialized Research Found for the Doctoral Program of Higher Education (20070699013)the Natural Science Foundation of Shaanxi Province (2006F05)the Aeronautical Science Foundation (05I53076)
文摘Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.
文摘A multi dimensional concatenation scheme for block codes is introduced, in which information symbols are interleaved and re encoded for more than once. It provides a convenient platform to design high performance codes with flexible interleaver size. Coset based MAP soft in/soft out decoding algorithms are presented for the F24 code. Simulation results show that the proposed coding scheme can achieve high coding gain with flexible interleaver length and very low decoding complexity.
文摘Synthetic aperture radar （SAR） imagery is a kind of coherent system that produces a random pattern, named speckle, which degrades the merit of SAR images and affects their further application seriously. Therefore, how to restore SAR image from the speckle has become a necessary step in post-processing of image. A new despeckling method is putforth on the basis of wavelet. First, a new approach on the basis of ＂second kind statistics＂ is used to estimate the dispersion parameter of the Cauchy distribution. Then, this Cauchy prior is applied to model the distribution of the wavelet coefficients for the log-transformed reflectance of SAR image. Based on the above ideas, a new homomorphic wavelet-based maximum a posterior （MAP） despeckling method is proposed. Finally, the simulated speckled image and the real SAR image are used to verify our proposed method and the results show that it outperforms the other methods in terms of the speckle reduction and the feature retention.
基金the National Natural Science Foundation of China(No90304001,60472036)the Beijing Natural Science Foundation(4052007)+1 种基金the National Key Lab of Communication Foundation,UEST,China(51434050105QT0101) the PolyU/UGC grants(B-Q698)
文摘Construction of high resolution images from low resolution sequences having rigid or semi-rigid ob-jects with unified motions is often important in surveillance and other applications.In this paper a novelobject-based super resolution reconstruction scheme was proposed,in which a six-parameter affine model-based object tracking and registration method was first used to segment and match objects among a se-quence of low resolution frames.The motion model was then further extended to the traditional maximuma posterior(MAP)super resolution algorithm.The proposed object tracking and registration method wasevaluated by both simulated and real acquired sequences.The results have demonstrated the high accura-cy of the proposed object based method and the enhanced reconstruction performance of the extended ap-proach.
文摘This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses 'Filters, Random and Maximum Entropy (Abb. FRAME)' model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an experiment with synthesized and real images is given, which shows that the method can segment images with complex textures efficiently and is robust to noise.
