Number Of Looks Research Articles

An optimized non-local means filter using automated clustering based preclassification through gap statistics for speckle reduction in breast ultrasound images

Speckle noise is a characteristic artifact in breast ultrasound images, which hinders substantive information essential for clinical diagnosis. In this article, we have investigated the use of Non-local means (NLM) filter, which is robust against severe noise, to remove speckle noise in breast ultrasound images. Medical diagnosis systems cannot employ traditional NLM filters, which exhibit the slowest performance due to their computational burden during the weighted averaging process. We have integrated a novel automated clustering based preclassification scheme using spatial regularized fuzzy c means (FCM) to alleviate the process. The appropriate number of clusters for each image is calculated automatically through Gap statistics. Moreover, the rotationally invariant moment distance measure increases the chance of getting more similar regions for NLM process. The algorithm is evaluated on a breast ultrasound database, which consists of 54 images including 28 benign and 26 malignant. Two statistical measures, Pratt’s figure of merit (PFM) and equivalent number of looks (ENL), are used to evaluate the noise suppression performance as well as the capability of preserving the fine details. The results of the proposed method are compared with the other three state of the art methods quantitatively. The proposed method demonstrated excellent despeckling performance with PFM of 0.91 and ENL of 7.415. The robustness against speckle noise and the acceptable processing time make the method more appropriate for computer aided diagnosis systems.

Comparative study of statistical modeling of wavelet coefficients for SAR image despeckling

The work is concentrated to get good despeckled performance under non-homomorphic framework by involving simple distributions to obtain closed form Maximum-a-Posteriori probability (MAP) solutions. To estimate noise-free wavelet coefficient, distributions with lesser number of parameters allied Laplacian/Gaussian probability density function (pdf) for signal reflectivity and Rayleigh/Gaussian pdf for noisy signal are employed. Thus, the four despeckling methods are formed, namely LRMAP, GRMAP, LGMAP and GGMAP to despeckle SAR images and the effectiveness of different distributions is studied. The despeckling method is made adaptive by estimating the local variance of high frequency image using wavelet sub-band coefficient statistics. Also, the parameter space used in the proposed methods does not involve initialization, iterative search and convergence problems. The performances of the proposed methods are evaluated in terms of Equivalent Number of Looks (ENL), Peak Signal to Noise Ratio (PSNR), Edge Preservation ([Formula: see text] and Mean Structural Similarity Index Measure (MSSIM). Experimental results show that LRMAP yields good results over all methods and GGMAP does not perform good for all images in terms of all quality metrics. Also, the proposed methods yield good quality metrics in less computing time as compared with the method available in the literature.

Fully Focused SAR Altimetry: Theory and Applications

In this paper, we introduce the concept, develop the theory, and demonstrate the advantages of fully focused coherent processing of pulse echoes from a nadir-looking pulse-limited radar altimeter. This process, similar to synthetic aperture radar (SAR) imaging systems, reduces the along-track resolution down to the theoretical limit equal to half the antenna length. We call this the fully focused SAR (FF-SAR) altimetry processing. The technique is directly applicable to SAR altimetry missions such as CryoSat-2, Sentinel-3, or Sentinel-6/Jason-CS. The footprint of an FF-SAR altimeter measurement is a narrow strip on the surface, which is pulse limited across track and SAR focused along track. Despite the asymmetry of the altimeter footprint, the fully focused technique may be useful for applications in which one needs to separate specific targets within highly heterogeneous scenes, such as in the case of sea ice lead detection, hydrology, and coastal altimetry applications. In addition, over rough homogeneous surfaces, such as the ocean or ice sheets, the improved multilooking capability of FF-SAR leads to a significant increase in the effective number of looks with respect to the delay/Doppler processing, resulting in better geophysical parameter estimation. The proposed processing technique was verified by processing full bit rate CryoSat-2 SAR mode data over the Svalbard transponder. Hydrology, sea ice, and open ocean applications are also demonstrated in this paper, showing the improvement of this technique with respect to conventional and delay/Doppler altimetry.

SAR Image Segmentation With Rényi's Entropy

Synthetic aperture radar (SAR) image segmentation is an important task in image processing. However, classic segmentation techniques are inadequate due to the presence of speckle noise. In this paper, we present a methodology for SAR image segmentation that uses the matrix of Renyi's entropy. This matrix arises from SAR data that follows the $G_A^0$ model, and here, it is an input to segmentation methods. For performance evaluation of the proposed methodology, we employ the error of segmentation, the cross-region fitting index, the Dice measure, as well as the rates of false positives and negatives. Tests have been performed on synthetic and real SAR images and the matrix of entropy has improved the results, regardless of the increase of the number of looks. The Otsu's method of global thresholding produced good segmentation results when applied to this matrix.

Multiscale and Multidirectional Multilooking for SAR Image Enhancement

With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.

Modeling the expected detection probability for mine countermeasure operations based on cross-section and multiple looks

We present a methodology to determine the expected probability of detection of a target based on multiple looks or angles: tactics and cross-sections. While previous studies confirm that the general probability of detection improves significantly with the number of looks, our findings demonstrate that this value can vary substantially depending on search and detection tactics, as well as the target’s cross-section. We argue that the angular dependence of the detection process is often overlooked for search and detection missions, but that this can be elegantly accounted for when assuming symmetrical targets. Consequently, we investigate three tactics where each look is independent: first, a globally optimal tactic where all look angles are equally spaced; second, a tactic which distributes the look angles randomly; and third, a tactic which imposes the same angle for all look angles. We are also able to use the first tactic to extract the guaranteed best angle of the broad side of a target, and additionally for each tactic we propose an example of a corresponding search pattern. We hope that our findings in this paper increase awareness within the search and detection community of the phenomenologies of multiple looks, and that our presented methodology can be incorporated into the mine search and detection tactics.

Adaptive anisotropic diffusion for noise reduction of phase images in Fourier domain Doppler optical coherence tomography.

Phase image in Fourier domain Doppler optical coherence tomography offers additional flow information of investigated samples, which provides valuable evidence towards accurate medical diagnosis. High quality phase images are thus desirable. We propose a noise reduction method for phase images by combining a synthetic noise estimation criteria based on local noise estimator (LNE) and distance median value (DMV) with anisotropic diffusion model. By identifying noise and signal pixels accurately and diffusing them with different coefficients respectively and adaptive iteration steps, we demonstrated the effectiveness of our proposed method in both phantom and mouse artery images. Comparison with other methods such as filtering method (mean, median filtering), wavelet method, probabilistic method and partial differential equation based methods in terms of peak signal-to-noise ratio (PSNR), equivalent number of looks (ENL) and contrast-to-noise ratio (CNR) showed the advantages of our method in reserving image energy and removing noise.

Novel estimators of equivalent number of looks in polarimetric SAR imagery based on sub-matrices

The equivalent number of looks (ENL) is an important parameter in the multilook statistical model of polarimetric synthetic aperture radar (Pol-SAR). Recently, the maximum likelihood (ML) method was proposed and gave a good performance in the Gaussian model case by using the full covariance matrix instead of the intensity of Pol-SAR data, but it generated underestimates in the product model case. In this paper several novel ENL estimators are presented via certain cumulants of the log-determinant of the sub-matrices of the multilook polarimetric covariance matrix. The texture effect to the ENL estimates is eliminated, and the analytic estimators are derived. The estimators use the full covariance matrix and sub-matrices information, rather than the intensities of polarization channels. All the novel estimators are suitable for any texture model and thus provide more accurate results than many existing ones. Experiments using simulated data and real data are presented to evaluate the performance of different estimators. The results show that the second log-determinant moment (SLDM3)-based method is the best one among the novel estimators. At the same time this estimator has much less computational complexity. In addition, a novel distribution classification method is proposed by coloring the image via second- and third-order log-cumulants of the covariance matrix (MLC), which is helpful to assess the estimation result.

Eye preferences in capuchin monkeys (Sapajus apella).

This study explored whether capuchin monkey eye preferences differ systematically in response to stimuli of positive and negative valence. The 'valence hypothesis' proposes that the right hemisphere is more dominant for negative emotional processing and the left hemisphere is more dominant for positive emotional processing. Visual information from each eye is thought to be transferred faster to and primarily processed by the contralateral cerebral hemisphere. Therefore, it was predicted capuchin monkeys would show greater left eye use for looking at negative stimuli and greater right eye use for looking at positive stimuli. Eleven captive capuchin monkeys were presented with four images of different emotional valence (an egg and capuchin monkey raised eyebrow face were categorised as positive, and a harpy eagle face and capuchin monkey threat face were categorised as negative) and social relevance (consisting of capuchin monkey faces or not), and eye preferences for viewing the stimuli through a monocular viewing hole were recorded. While strong preferences for using either the left or right eye were found for most individuals, there was no consensus at the population level. Furthermore, the direction of looking, number of looks and duration of looks did not differ significantly with the emotional valence of the stimuli. These results are inconsistent with the main hypotheses about the relationship between eye preferences and processing of emotional stimuli. However, the monkeys did show significantly more arousal behaviours (vocalisation, door-touching, self-scratching and hand-rubbing) when viewing the negatively valenced stimuli than the positively valenced stimuli, indicating that the stimuli were emotionally salient. These findings do not provide evidence for a relationship between eye preferences and functional hemispheric specialisations, as often proposed in humans. Additional comparative studies are required to better understand the phylogeny of lateral biases, particularly in relation to emotional valence.

SAR image despeckling using heavy-tailed Burr distribution

Multiplicative speckle noise diminishes the radiometric resolution of the synthetic aperture radar (SAR) images and all the coherent images. Speckle removal adds an extra value to an automated SAR image interpretation and analysis. In this paper, dual-tree complex wavelet-transform-based Bayesian method is proposed for despeckling the SAR images. In each subband, the reflectance and noise of the logarithmically transformed wavelet coefficients are modeled using heavy-tailed Burr and zero-mean Gaussian distributions. The closed-form expression for the shape parameter of Burr distribution is derived by employing the Mellin transform. The resultant complex-free quadratic maximum a posteriori solution with suitable shrinkage function yields despeckled SAR images. Extensive experiments are carried out using real SAR images as well as simulated images. The proposed method performs well in terms of equivalent number of looks with 3.5751 dB improvement in homogeneous region1 of Pipe river SAR image, edge preservation with 0.6158 improvement, peak signal to noise ratio of 51.3305 dB, and mean structural similarity index measure of 0.9397 at 0.05 noise variance for synthetically speckled image in comparison to the existing methods and takes averagely 2.3461 times less computing time. The proposed method provides a computationally efficient better speckle reduction in homogeneous regions while still preserving the edge.

Study of Subsidence detection by DinSAR and evaluation of some factors to the outcome

Study of Subsidence detection by DinSAR and evaluation of some factors to the outcome

An enhanced p-norm energy detector for coherent multilook detection in X-band maritime surveillance radar

This paper introduces a new variation of the p-norm detector, which is designed for application to coherent multilook detection in compound Gaussian clutter with inverse Gamma texture. By applying what is termed a compensator, enhanced detection performance can be achieved independently of the number of looks used. This is particularly useful in the case of a fast scan rate radar where the number of looks may be quite small. Conventional coherent detectors tend to experience saturation in such scenarios, and so this new detection process complements recent advances in this area. Further validation is provided by applying this new decision rule to synthetic target detection in real X-band radar clutter.

Separability of sea ice types from wide swath C- and L-band synthetic aperture radar imagery acquired during the melt season

Abstract Differentiating between first-year ice (FYI) and multi-year ice (MYI) in C-band synthetic aperture radar (SAR) imagery during spring–summer melt, when wet snow and melt ponds mask the underlying ice, is difficult. It has been suggested that the use of L-band SAR may alleviate this concern given increased penetration depths at longer wavelengths; however, this has not been thoroughly assessed. Here the separability of FYI and MYI is compared using horizontally polarized (HH) C-band (RADARSAT-2) and L-band (ALOS/PALSAR) ScanSAR images acquired over landfast sea ice in the Canadian Arctic Archipelago in the spring and summer of 2009. L-band provided enhanced contrast between FYI and MYI during early melt onset and during the drainage phase of advanced melt, while C-band was found to provide enhanced contrast when the wet snowpack was transitioning from the pendular regime to the funicular regime. At the time of the pendular–funicular transition, the backscatter signatures of FYI and MYI reversed at both C- and L-band. This behavior is well established at C-band, but has not been reported previously at L-band. The L-band imagery also provided improved definition of floe boundaries and ridges throughout the melt season. Finally, the L-band data had reduced speckle (equivalent number of looks ~ 12), relative to the C-band data (~ 9 equivalent looks). These results indicate that L-band SAR data acquired during the melt season could be used to enhance operational and scientific sea ice information products that have traditionally been derived from single-frequency C-band SAR data.

Speckle Removal from SAR Images in the Lapped Transform Domain Using Adaptive Threshold Based on Despeckling Evaluation Indexes

We propose in this paper an efficient and adaptive threshold estimation method for suppressing speckle in synthetic aperture radar (SAR) images, based on Lapped transform (LT). Lapped orthogonal transform (LOT) is robust to oversmoothing, can preserve texture components better and has less computational complexity than orthogonal discrete wavelet transform (DWT). Since LOT is block transform, the dyadic remapping of transform coefficients are carried out first and then the subband coefficients are thresholded. We estimate the ‘optimal’ threshold value using two despeckling performance indexes namely equivalent number of looks (ENL) and edge saving index (ESI). The threshold value is then used in soft thresholding function to shrink the noisy LOT coefficients. Due to the use of despeckling performance indexes in threshold estimation, the test on real SAR images show that the proposed method notably suppresses speckle with good edge preservation and achieves better performance than other related methods.

Compensated Row-Column Ultrasound Imaging System Using Fisher Tippett Multilayered Conditional Random Field Model.

3-D ultrasound imaging offers unique opportunities in the field of non destructive testing that cannot be easily found in A-mode and B-mode images. To acquire a 3-D ultrasound image without a mechanically moving transducer, a 2-D array can be used. The row column technique is preferred over a fully addressed 2-D array as it requires a significantly lower number of interconnections. Recent advances in 3-D row-column ultrasound imaging systems were largely focused on sensor design. However, these imaging systems face three intrinsic challenges that cannot be addressed by improving sensor design alone: speckle noise, sparsity of data in the imaged volume, and the spatially dependent point spread function of the imaging system. In this paper, we propose a compensated row-column ultrasound image reconstruction system using Fisher-Tippett multilayered conditional random field model. Tests carried out on both simulated and real row-column ultrasound images show the effectiveness of our proposed system as opposed to other published systems. Visual assessment of the results show our proposed system’s potential at preserving detail and reducing speckle. Quantitative analysis shows that our proposed system outperforms previously published systems when evaluated with metrics such as Peak Signal to Noise Ratio, Coefficient of Correlation, and Effective Number of Looks. These results show the potential of our proposed system as an effective tool for enhancing 3-D row-column imaging.

Adaptive method of speckle reduction based on curvelet transform and thresholding neural network in synthetic aperture radar images

K.N. Toosi University of Technology, Department of Geomatics Engineering, No. 1346,ValiAsr Street, Mirdamad Cross, Tehran, IranAbstract. Because of the effect of speckles in synthetic aperture radar (SAR) images, its reduc-tionhasbeenconsideredbymanyresearcherstoobtainreliableinformation.Thispaperproposesa method based on the curvelet transform to reduce speckles in SAR images. This study is basedonthethresholdingneural network(TNN)technique,which hasbeenpreviouslyusedinwavelettransformation. In addition, an adaptive learning TNN with remarkable time saving was intro-duced.Comparingtheobtainedresultsfrom themethodwithconventionalspecklefilterssuchasLee, Kuan, Frost, and Gamma filters, curvelet-based, nonadaptive despeckling, wavelet-basedTNN despeckling, and curvelet-based particle swarm optimization show better achievement ofthe proposed algorithm. For instance, noise mean value, noise standard deviation, mean squaredifference, equivalent number of looks, and β (an edge-preserving criterion) improved 2%, 9%,21%, 35%, and 9%, respectively.

Estimation of the Equivalent Number of Looks in SAR Images Based on Singular Value Decomposition

In this letter, a singular value decomposition (SVD)-based method for estimating the equivalent number of looks (ENL) in synthetic aperture radar (SAR) images is proposed. First, SAR images are logarithmically scaled to change the multiplicative speckle into additive noise. Assuming that the multiplicative speckle is a gamma random variable with unit mean, a monotone function is established between the ENL and the variance of the additive noise, thus transforming the ENL estimation problem into estimating the variance of the additive noise. Then SVD is applied to the logarithmic image in a window with certain size to obtain its singular values. By making use of an empirical linear relationship between the average of the smallest singular values and the standard deviation of the noise in the logarithmic image, the variance of the additive noise is finally estimated. Experiments of both simulated and real SAR images demonstrated the effectiveness of the proposed method.

Adaptive-Window Polarimetric SAR Image Speckle Filtering Based on a Homogeneity Measurement

This paper proposes a polarimetric homogeneity measurement and applies it to the speckle filtering of polarimetric synthetic aperture radar (PolSAR) data. First, a line-and-edge (LAE) detector that can detect both the lines and edges in one scan is developed based on the traditional edge detector. A polarimetric homogeneity measurement is then derived by combining the equivalent number of looks and the LAE maps and is used to distinguish the homogeneous and heterogeneous regions. Finally, a new adaptive-window PolSAR filtering algorithm based on the LAE detector and the polarimetric homogeneity measurement is proposed. The proposed speckle filter adjusts the filtering windows in both shape and size, based on the homogeneity and gradient information. Consequently, it uses small and nonsquare windows in heterogeneous regions to preserve the detail information and uses large and square windows in homogeneous regions to maximize the suppression of speckle noise. EMISAR and ESAR L-band PolSAR data were used to demonstrate the effectiveness of the proposed filter in speckle suppression, detail preservation, and polarimetric information preservation.

Speckle noise reduction algorithm with total variation regularization in optical coherence tomography

Optical coherence tomography (OCT) is an important imaging technique extensively applied in medical sciences. However, OCT images often suffer from speckle noise, which is a kind of multiplicative noise inherited in coherent imaging systems. A speckle noise reduction algorithm with total variation (TV) regularization is proposed to restore speckled OCT images. It constructs the regularization parameter and utilizes the tuning function. The proposed algorithm realizes sufficient speckle noise reduction and delicate edge preservation. Simulations demonstrate the performance of the proposed algorithm with respect to visual effects, processing time and image quality metrics of signal-to-noise ratio (SNR), equivalent number of looks (ENL), contrast-to-noise ratio (CNR), relative mean square error (RMSE) and edge preservation factor. Compared with some classical and typical despeckling algorithms, the proposed algorithm exhibits good results in edge preservation, recovery error and time efficiency, and presents better SNR, ENL and CNR. The applicability of the proposed algorithm with regard to OCT in-device preprocessing is discussed in details. Therefore, it promotes the application of OCT imaging in clinical diagnosis and analysis.

Characterization and Correction of Multilook Effects on Eigendecomposition Parameters in PolSAR Images

The eigendecompostion of the coherency matrix and the related parameters, i.e., entropy $H$ , $\alpha$ , and anisotropy $A$ , are effective and popular tools for the analysis and quantitative estimation of the physical parameters of polarimetric synthetic aperture radar images. However, the speckle noise constitutes the main obstacle that hinders these goals and should be filtered. In this paper, based on studies obtained from extensive simulated data sets, we tried to determine how this noise is transmitted to sample eigendecomposition parameters. The dependence between means and variances of sample parameters leads to building their speckle models and to the definition of a bias elimination procedure. We found that sample eigenvalues were affected by multiplicative noise, whereas sample entropy and sample anisotropy were affected by a mixture of multiplicative and additive noise sources. In addition to its versatility, independence to knowledge of the equivalent number of looks and high ability to bias compensation, the proposed bias suppression technique reduced the variance of noise. Simulated and real data as well as the existing theories are used for validation in this paper.