Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm

A maximum a posteriori（ MAP） algorithm is proposed to improve the accuracy of super resolution（ SR） reconstruction in traditional methods. The algorithm applies both joints image registration and SR reconstruction in the framework,but separates them in the process of iteratiion. Firstly,we estimate the shifting parameters through two lowresolution（ LR） images and use the parameters to reconstruct initial HR images. Then,we update the shifting parameters using HR images. The aforementioned steps are repeated until the ideal HR images are obtained. The metrics such as PSNR and SSIM are used to fully evaluate the quality of the reconstructed image. Experimental results indicate that the proposed method can enhance image resolution efficiently.

MAXIMUM A POSTERIORI-BASED AUTOMATIC TARGET DETECTION IN SAR IMAGES

The paper presents an algorithm of automatic target detection in Synthetic Aperture Radar(SAR) images based on Maximum A Posteriori(MAP). The algorithm is divided into three steps. First, it employs Gaussian mixture distribution to approximate and estimate multi-modal histogram of SAR image. Then, based on the principle of MAP, when a priori probability is both unknown and learned respectively, the sample pixels are classified into different classes c = {target,shadow, background}. Last, it compares the results of two different target detections. Simulation results preferably indicate that the presented algorithm is fast and robust, with the learned a priori probability, an approach to target detection is reliable and promising.

INTERFEROGRAM NOISE REDUCTION ALGORITHM BASE ON MAXIMUM A POSTERIORI ESTIMATE

Interferogram noise reduction is a very important processing step in Interferometric Synthetic Aperture Radar(InSAR) technique. The most difficulty for this step is to remove the noises and preserve the fringes simultaneously. To solve the dilemma, a new interferogram noise reduction algorithm based on the Maximum A Posteriori(MAP) estimate is introduced in this paper. The algorithm is solved under the Total Generalized Variation(TGV) minimization assumption, which exploits the phase characteristics up to the second order differentiation. The ideal noise-free phase consisting of piecewise smooth areas is involved in this assumption, which is coincident with the natural terrain. In order to overcome the phase wraparound effect, complex plane filter is utilized in this algorithm. The simulation and real data experiments show the algorithm can reduce the noises effectively and meanwhile preserve the interferogram fringes very well.

Research on Adaptive Optics Image Restoration Algorithm Based on Improved Joint Maximum a Posteriori Method

In this paper, we propose a point spread function （PSF） reconstruction method and joint maximum a posteriori （JMAP） estimation method for the adaptive optics image restoration. Using the JMAP method as the basic principle, we establish the joint log likelihood function of multi-frame adaptive optics （AO） images based on the image Gaussian noise models. To begin with, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect is developed; then, we build up iterative solution formulas of the AO image based on our proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. We conduct a series of experiments on simulated and real degraded AO images to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution （Wiener-IBD） algorithm and Richardson-Lucy IBD algorithm, our algorithm has better restoration effects including higher peak signal-to-noise ratio （PSNR） and Laplacian sum （LS） value than the others. The research results have a certain application values for actual AO image restoration.

Region-based classification by combining MS segmentation and MRF for POLSAR images

Speckle effects on classification results can be sup- pressed to some extent by introducing the contextual information. An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar （POLSAR） images based on the mean shift （MS） segmentation and Markov random field （MRF）. First, polarimetdc features are exacted by target decomposition for MS segmentation. An initial classification is executed by using the target decomposition and the agglomerative hierarchical clus- tering algorithm. Thereafter, a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment. Under the MRF framework, the smoothness term is defined according to the distance between neighboring areas. By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory, the experimental results confirm that the proposed method has higher accuracy and better regional connectivity than other classification methods.

Joint MAP channel estimation and data detection for OFDM in presence of phase noise from free running and phase locked loop oscillator

This paper addresses a computationally compact and statistically optimal joint Maximum a Posteriori(MAP)algorithm for channel estimation and data detection in the presence of Phase Noise(PHN)in iterative Orthogonal Frequency Division Multiplexing(OFDM)receivers used for high speed and high spectral efficient wireless communication systems.The MAP cost function for joint estimation and detection is derived and optimized further with the proposed cyclic gradient descent optimization algorithm.The proposed joint estimation and detection algorithm relaxes the restriction of small PHN assumptions and utilizes the prior statistical knowledge of PHN spectral components to produce a statistically optimal solution.The frequency-domain estimation of Channel Transfer Function(CTF)in frequency selective fading makes the method simpler,compared with the estimation of Channel Impulse Response(CIR)in the time domain.Two different time-varying PHN models,produced by Free Running Oscillator(FRO)and Phase-Locked Loop(PLL)oscillator,are presented and compared for performance difference with proposed OFDM receiver.Simulation results for joint MAP channel estimation are compared with Cramer-Rao Lower Bound(CRLB),and the simulation results for joint MAP data detection are compared with“NO PHN"performance to demonstrate that the proposed joint MAP estimation and detection algorithm achieve near-optimum performance even under multipath channel fading.

New Iterated Decoding Algorithm Based on Differential Frequency Hopping System

A new iterated decoding algorithm is proposed for differential frequency hopping (DFH) encoder concatenated with multi-frequency shift-key (MFSK) modulator. According to the character of the frequency hopping (FH) pattern trellis produced by DFH function, maximum a posteriori (MAP) probability theory is applied to realize the iterate decoding of it. Further, the initial conditions for the new iterate algorithm based on MAP algorithm are modified for better performance. Finally, the simulation result compared with that from traditional algorithms shows good anti-interference performance.

Several Kinematic Data Processing Methods for Time-Correlated Observations

Time correlations always exist in modern geodetic data,and ignoring these time correlations will affect the precision and reliability of solutions.In this paper,several methods for processing kinematic time-correlated observations are studied.Firstly,the method for processing the time-correlated observations is expanded and unified.Based on the theory of maximum a posteriori estimation,the third idea is proposed after the decorrelation transformation and differential transformation.Two types of situations with and without common parameters are both investigated by using the decorrelation transformation,differential transformation and maximum a posteriori estimation solutions.Besides,the characteristics and equivalence of above three methods are studied.Secondly,in order to balance the computational efficiency in real applications and meantime effectively capture the time correlations,the corresponding reduced forms based on the autocorrelation function are deduced.Finally,with GPS real data,the correctness and practicability of derived formulae are evaluated.

A Combined Speaker Adaptation Method for Mandarin Speech Recognition

A speaker adaptation method that combines transformation matrix linear interpolation with maximum a posteriori (MAP) was proposed. Firstly this method can keep the asymptotical characteristic of MAP. Secondly, as the method uses linear interpolation with several speaker-dependent (SD) transformation matrixes, it can fully use the prior knowledge and keep fast adaptation. The experimental results show that the combined method achieves an 8.24% word error rate reduction with only one adaptation utterance, and keeps asymptotic to the performance of SD model for large amounts of adaptation data.

ITERATIVE RECEIVER FOR OFDM SYSTEMS BASED ON SEMI-BLIND CHANNEL ESTIMATION

In this paper we propose two iterative algorithms of joint channel estimation and symbol detection for Orthogonal Frequency Division Multiplexing (OFDM) systems. In which, superimposed pilot scheme is adopted and an initial Channel State Information (CSI) is obtained by employing a first-order statistic. In each subsequent iteration, we propose two algorithms to update the CSI. The Mean Square Error (MSE) of channel estimation and Bit Error Rate (BER) performance are given and simulation results demonstrate that the iterative algorithm using method B has good perform-ance approaching the ideal condition.

MAP based blocking artifact reduction for DCT domain distributed video coding

This paper proposes a maximum a posteriori （MAP） based blocking artifact reduction algorithm for discrete cosine transform （DCT） domain distributed video coding, in which the SI and the initial reconstructed Wyner-Ziv （WZ） frame are utilized to further estimate the original WZ frame. Though the MAP estimate improves quality of the artifact region, it also leads to over-smoothness and decreases quality of the non-artifact region. To overcome this problem, a criterion is presented to discriminate the artifact and the non-artifact region in the initial reconstructed WZ frame, and only the artifact region is updated with the MAP estimate. Simulation results show that the proposed algorithm provides obvious improvement in terms of both objective and subjective evaluations.

Maximum Likelihood A Priori Knowledge Interpolation-Based Handset Mismatch Compensation for Robust Speaker Identification

Unseen handset mismatch is the major source of performance degradation in speaker identification in telecommunication environments. To alleviate the problem, a maximum likelihood a priori knowledge interpolation （ML-AKI）-based handset mismatch compensation approach is proposed. It first collects a set of handset characteristics of seen handsets to use as the a priori knowledge for representing the space of handsets. During evaluation the characteristics of an unknown test handset are optimally estimated by interpolation from the set of the a priori knowledge. Experimental results on the HTIMIT database show that the ML-AKI method can improve the average speaker identification rate from 60.0% to 74.6% as compared with conventional maximum a posteriori-adapted Gaussian mixture models. The proposed ML-AKI method is a promising method for robust speaker identification.

Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random feld modeling

Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.

A Super-resolution Reconstruction Algorithm for Surveillance Video

Recent technological developments have resulted in surveillance video becoming a primary method of preserving public security.Many city crimes are observed in surveillance video.The most abundant evidence collected by the police is also acquired through surveillance video sources.Surveillance video footage offers very strong support for solving criminal cases,therefore,creating an effective policy and applying useful methods to the retrieval of additional evidence is becoming increasingly important.However,surveillance video has had its failings,namely,video footage being captured in low resolution(LR)and bad visual quality.In this paper,we discuss the characteristics of surveillance video and describe the manual feature registration-maximum a posteriori-projection onto convex sets to develop a super-resolution reconstruction method,which improves the quality of surveillance video.From this method,we can make optimal use of information contained in the LR video image,but we can also control the image edge clearly as well as the convergence of the algorithm.Finally,we make a suggestion on how to adjust the algorithm adaptability by analyzing the prior information of target image.

A Markov Random Field Model-Based Approach to Natural Image Matting

This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions. In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel. All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori （MAP） estimation problem. Simulated annealing is used to find the optimal MAP estimation. The better results can be obtained under the same user-interactions when images are complex. Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.

Image Denoising via Improved Simultaneous Sparse Coding with Laplacian Scale Mixture

Image denoising is a well-studied problem closely related to sparse coding. Noticing that the Laplacian distribution has a strong sparseness, we use Laplacian scale mixture to model sparse coefficients. With the observation that prior information of an image is relevant to the estimation of sparse coefficients, we introduce the prior information into maximum a posteriori（MAP） estimation of sparse coefficients by an appropriate estimate of the probability density function. Extending to structured sparsity, a nonlocal image denoising model： Improved Simultaneous Sparse Coding with Laplacian Scale Mixture（ISSC-LSM） is proposed. The centering preprocessing, which admits biased-mean of sparse coefficients and saves expensive computation, is done firstly. By alternating minimization and learning an orthogonal PCA dictionary, an efficient algorithm with closed-form solutions is proposed. When applied to noise removal, our proposed ISSC-LSM can capture structured image features, and the adoption of image prior information leads to highly competitive denoising performance. Experimental results show that the proposed method often provides higher subjective and objective qualities than other competing approaches. Our method is most suitable for processing images with abundant self-repeating patterns by effectively suppressing undesirable artifacts while maintaining the textures and edges.

Efficient detection for cooperative communication with two alternating relays

A two-path amplify-and-forward(AF)relaying scheme,which uses two relays to retransmit for the source alternately,was proposed by Rankov[Rankov et al.IEEE Journal on Selected Areas in Communications,2007,25(2):379–389].This scheme can avoid half-duplex loss in spectral efficiency,but it cannot work well when the interrelay channel gain is strong.An efficient detection scheme for two-path AF relaying system is proposed in this paper.In the proposed scheme,interference cancellation is first performed at the destination so that the received signal after interference cancellation is the superposition of two symbols with noise.Then,a low complexity soft maximum a posteriori(MAP)decoder and an iterative soft decoder are employed to extract the diversity of the two relay-destination links.The proposed detection scheme can efficiently improve the system performance compared to the detection scheme presented by Rankov(2007),especially when the inter-relay channel gain is strong.