Contourlet-based despeckling for SAR image using hidden Markov tree and Gaussian Markov models

Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training to fit an HMT model to a given data set (e.g., using the expectation-maximization algorithm). We greatly simplify the HMT model by exploiting the inherent self-similarity of real-world images. The simplified model specifies the HMT parameters with just nine meta-parameters (independent of the size of the image and the number of wavelet scales). We also introduce a Bayesian universal HMT (uHMT) that fixes these nine parameters. The uHMT requires no training of any kind, while extremely simple, we show using a series of image estimation/denoising experiments that these new models retain nearly all of the key image structure modeled by the full HMT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperforms other wavelet-based estimators in the current literature, both visually and in mean square error.

In this paper, we present a framework for functional MR image restoration based on the Hidden Markov Tree (HMT) model. Under this scheme, the wavelet/contourlet coefficients of the distorted image are filtered using the HMT model of the baseline image to minimise the statistical divergence between two images. An iterative algorithm between image registration and HMT filtering is developed to achieve a trade-off between the least mean square error (in the spatial domain) and the minimum statistical divergence (in the spectral domain). We demonstrate that the proposed method can eliminate the motion artefacts (such as spikes and burring) in the Functional MR Imaging data more effectively, leading to reliable neural activity detection. This method can also be used for image restoration in other medical imaging applications.

The authors propose a multiscale Bayesian texture segmentation algorithm that is based on a complex wavelet domain hidden Markov tree (HMT) model and a hybrid label tree (HLT) model. The HMT model is used to characterise the statistics of the magnitudes of complex wavelet coefficients. The HLT model is used to fuse the interscale and intrascale context information. In the HLT, the interscale information is fused according to the label transition probability directly resolved by an EM algorithm. The intrascale context information is also fused so as to smooth out the variations in the homogeneous regions. In addition, the statistical model at pixel-level resolution is formulated by a Gaussian mixture model (GMM) in the complex wavelet domain at scale 1, which can improve the accuracy of the pixel-level model. The experimental results on several texture images are used to evaluate the algorithm.

The wavelet-domain hidden Markov tree (HMT) model provides a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. However, it is usually assumed that the subbands at the same level are independent in the traditional HMT model. This paper proposes a modified HMT model, SHMT-S, in which a vector constructed from the coefficients at the same location of the subbands of the same level, is controlled by a hidden state. Meanwhile we also use the vector Laplace mixture distributions to fit the wavelet coefficients vector, which is peakier in the center and has heavier tails compared with Gaussian distribution. By using the HMT segmentation framework, we develop SHMT-S based segmentation methods for image textures and dynamic textures. The experimental results demonstrate the effectiveness of the proposed method.

This paper proposes a novel method on Synthetic Aperture Radar (SAR) Image denoising, which is based on context modeling combined with multiscale orthogonal bandlet coefficients. For the main influence of SAR image is multiplicative speckle noise, the logarithm uniform transform is used to convert it to additive noise. In this paper, the multiscale orthogonal bandlet transform is applied to noisy SAR Image. The contextual model is used to compute contextual values of bandlet coefficients on each scale. Therefore, bandlet coefficients are reconstructed into some parts by contextual values. The spatially adaptive soft-threshold (T) is to denoise SAR image in each part. Experimental results demonstrate that this method outperforms the methods of Ehance-Lee (E-lee), contextual model based on wavelet domain (Wavelet-context), and Hidden Markov Tree model bases on wavelet domain (Wavelet-hmt) on there indexes: Floating index (FI), Standard deviation and Edge saving index of vertical direction, by using a real SAR image. However, the method can not only reduce the speckle noise but also preserve edges and high frequency information, and gets considerable visual effect on SAR Image denoising.

The uniform discrete curvelet transform (UDCT) is a variant of the discrete curvelet transform with basis function located on a uniform grid. The transform is implemented based on a filter bank structure in the frequency domain. In this paper, we study statistics and dependency analysis of the UDCT coefficients and impose the hidden Markov tree (HMT) model for the UDCT. Finally, the experiment in image denoising is shown as an application of the modeling.

This paper proposes a new method for SAR (Synthetic Aperture Radar) image despeckling based on statistical model of wavelet coefficients combined with modification to them according to maximum-modulus criterion. In the method, wavelet coefficients of logarithmic image are firstly modeled as mixture density of two Gaussian (MG) distributions with zero mean. Secondly, in order to incorporate the spatial dependencies into the despeckling procedure, Hidden Markov Tree model (HMT) is explored and Expectation Maximization (EM) algorithm is adopted to estimate model parameters. Bayes Minimum mean square error (Bayes MMSE) method is used to estimate the wavelet coefficients free of noise. The wavelet coefficients are updated according to a criterion whether the coefficient is a significant one or not. 2D inverse DWT and exponential transform are performed on the updated coefficients to get denoised SAR image. Experimental Results using real SAR images demonstrate that the method can not only reduce the speckle but also preserve edges and radiometric scatter points.

A semi-supervised hidden Markov tree (HMT) model is developed for texture analysis, incorporating both labeled and unlabeled data for training; the optimal balance between labeled and unlabeled data is estimated via the homotopy method. In traditional EM-based semi-supervised modeling, this balance is dictated by the relative size of labeled and unlabeled data, often leading to poor performance. Semi-supervised modeling may be viewed as a source allocation problem between labeled and unlabeled data, controlled by a parameter lambda isin [0,1], where lambda = 0 and 1 correspond to the purely supervised HMT model and purely unsupervised HMT-based clustering, respectively. We consider the homotopy method to track a path of fixed points starting from lambda = 0, with the optimal source allocation identified as a critical transition point where the solution is unsupported by the initial labeled data. Experimental results on real textures demonstrate the superiority of this method compared to the EM-based semi-supervised HMT training

We introduce a new image texture segmentation algorithm, HMTseg, based on wavelet-domain hidden Markov tree (HMT) models. The HMT model is a tree-structured probabilistic graph that captures the statistical properties of wavelet coefficients. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides provides a good classifier for textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform multiscale texture classification at various scales. We then fuse these multiscale classifications using a Bayesian probabilistic graph to obtain a reliable final segmentation. Since HMTseg works on the wavelet transform of the image, it can directly segment wavelet-compressed images, without the need for decompression. We demonstrate the performance of HMTseg with synthetic, aerial photo, and document image segmentations.

To segment regions of interest (ROIs) from ultrasound images, one novel dynamic texture based algorithm is presented with surfacelet transform, hidden Markov tree (HMT) model and parallel computing. During surfacelet transform, the image sequence is decomposed by pyramid model, and the 3D signals with high frequency are decomposed by directional filter banks. During HMT modeling, distribution of coefficients is described with Gaussian mixture model (GMM), and relationship of scales is described with scale continuity model. From HMT parameters estimated through expectation maximization, the joint probability density is calculated and taken as feature value of image sequence. Then ROIs and non-ROIs in collected sample videos are used to train the support vector machine (SVM) classifier, which is employed to identify the divided 3D blocks from input video. To improve the computational efficiency, parallel computing is implemented with multi-processor CPU. Our algorithm has been compared with the existing texture based approaches, including gray level co-occurrence matrix (GLCM), local binary pattern (LBP), Wavelet, for ultrasound images, and the experimental results prove its advantages of processing noisy ultrasound images and segmenting higher accurate ROIs.

We address the application of model-supported exploitation techniques to synthetic aperture radar (SAR) imagery. The emphasis is on monitoring SAR imagery using wide area 2D and/or 3D site models along with contextual information. We consider here the following tasks useful in monitoring: (a) site model construction using segmentation and labeling techniques, (b) target detection, (c) target classification and indexing, and (d) SAR image-site model registration. The 2-D wide area site models used here for SAR image exploitation differ from typical site models developed for RADIUS applications, in that they do not model specific facilities, but constitute wide area site models of cultural features such as urban clutter areas, roads, clearings, fields, etc. These models may be derived directly from existing site models, possibly constructed from electro-optical (EO) observations. When such models are not available, a set of segmentation and labeling techniques described here can be used for the construction of 2D site models. The use of models can potentially yield critical information which can disambiguate target signatures in SAR images. We address registration of SAR and EO images to a common site model. Specific derivations are given for the case of registration within the RCDE platform. We suggest a constant false alarm rate (CFAR) detection scheme and a topographic primal sketch (TPS) based classification scheme for monitoring target occurrences in SAR images. The TPS of an observed target is matched against candidate targets TPSs synthesized for the preferred target orientation, inferred from context (e.g. road or parking lot targets). Experimental results on real and synthetic SAR images are provided.

The SEASAT satellite system, using an onboard 23-cm-wavelength synthetic aperture radar (SAR), collected approximately 25- to 40-m-resolution radar images of the ocean in 100-km-wide swaths ranging in length from about 300 to 3000 km. Here we report results from the 18 SEASAT SAR passes during the Joint Air-Sea Interaction (JASIN) experiment conducted off the west coast of Scotland in the summer of 1978. These many SAR images, when coupled with the intensive ship, buoy, and aircraft measurements of the JASIN experiment, provide a unique opportunity to assess the ability of satellite SAR to measure ocean surface phenomena, particularly surface wave fields. In this study we use only optically processed SAR images. Although gravity waves of length approximately 80 to 300 m are often seen in SAR imagery, they are not always seen. We find that SAR resolution and wave height are important criteria for determining wave visibility and suggest that wind velocity is also. Comparisons between SAR and buoy estimates of dominant wavelength and direction (including data from several other experiments) agree to within about ±14% and ± 10°, respectively. We use a focus sharpness algorithm to resolve the 180° directional ambiguity of SAR image directional estimates. Correlation of buoy measurements of significant wave height (H 1/3) with peak signal- to-noise ratio in Fourier transforms of SAR images (r = 0.7) suggests that H 1/3 can be estimated to an accuracy of about ± 1 m. However, a similar, but weaker, correlation exists between signal-to-noise and wave length, i.e., H 1/3 and wavelength are correlated in the JASIN data set. SAR image power spectra are in rough agreement with buoy measurements of omnidirectional ocean wave height spectra Ψ(K) and correspond less closely with ocean wave slope spectra Ψ(K) However, the dominant wavenumber (Kpeak.) in SAR spectra typically falls below the dominant wavenumber of corresponding buoy spectra. Further, the slope of SAR spectra for K K Peak is typically less steep than corresponding buoy spectra Ψ(K). These differences between SAR spectra and buoy measurements of form qualitative support for the imaging mechanisms developed by Alpers, Ross, and Rufenach. Analysis algorithms based on this theory, but including empirical modifications, should substantially improve estimates of Ψ (K,θ) using SAR images.

This paper proposes using a backpropagation (BP) neural network for the classification of ship targets in airborne synthetic aperture radar (SAR) imagery. The ship targets consisted of 2 destroyers, 2 cruisers, 2 aircraft carriers, a frigate and a supply ship. A SAR image simulator was employed to generate a training set, a validation set, and a test set for the BP classifier. The features required for classification were extracted from the SAR imagery using three different methods. The first method used a reduced resolution version of the whole SAR image as input to the BP classifier using simple averaging. The other two methods used the SAR image range profile either before or after a local-statistics noise filtering algorithm for speckle reduction. Performance on an extensive test set demonstrated the performance and computational advantages of applying the neural classification approach to targets in airborne SAR imagery. Improvements due to the use of multi-resolution features were also observed.

Synthetic aperture radar (SAR) images are corrupted by speckle noise due to random interference of electromagnetic waves. In this paper, we proposed a speckle reduction technique based on sparse representation and dictionary learning. Firstly, an adaptive dictionary was learned by performing KSVD algorithm through a large amount of training patches extracted from the noisy SAR image. Considering the inaccurate recovery of point targets which is brought by the inadequate number of training samples, we employed a point target enhancing scheme to highlight the important point targets in the SAR image. Some experiments were conducted on real SAR images, and the results shows that our proposed algorithm can effectively reduce the speckle noise as well as preserve details. Some comparisons are made to prove its superiority to the available algorithms.

Contourlet-domain hidden markov tree(HMT) model can reflect the coefficients' dependencies of different scales and directions, then a SAR and optical image fusion algorithm based on it is proposed. Firstly source images are decomposed by Contourlet transform, low frequency and high frequency subband coefficients are obtained; secondly, the high frequency subband coefficients are modeled using hidden markov tree and the model is trained using Expectation-Maximization(EM) algorithm to get the posterior probability of the coefficients; Thirdly, using the posterior probability to guide the fusion rules' design of high frequency subband coefficients in order to preserve the salient features of original images well and obtain good noise suppression; Finally, the fusion coefficients are inversed to get the final fusion result. SAR and Panchromatic images were taken to fuse, and the results were evaluated by Difference Coefficient, Correlation Coefficient and Signal-Noise Ration. The experimental result shows that the proposed algorithm is better than traditional fusion algorithms based on Contourlet and wavelet-domain hidden markov tree model.