Equivalent Number Of Looks Research Articles

SPECKLE REDUCTION IN SAR IMAGES USING A BAYESIAN MULTISCALE APPROACH

Abstract. Synthetic aperture radar (SAR) images are corrupted by speckles, which influence the interpretation of the images. Therefore, to reduce speckles and obtain reliable information from images, researchers studied different methods. This study proposes a Bayesian multiscale method, to reduce speckles in SAR images. First, it was shown that Laplacian probability density function can capture the characteristics of noise-free curvelet coefficients, and then, a maximum a posteriori (MAP) estimator was designed for estimating them. Comparison of the results obtained with those obtained from conventional speckle filters, such as Lee, Kuan, Frost, and Gamma filters, and also curvelet non-Bayesian despeckling, shows better achievement of the proposed algorithm. For instance, the improvement in different parameters is as follows: ‘noise mean value’ (NMV) 0.24 times, ‘noise standard deviation’ (NSD) 0.34 times, ‘mean square difference’ (MSD) 2.6 times and ‘equivalent number of looks’ (ENL) 0.61 times.

Speckle reduction in visible-light optical coherence tomography using scan modulation.

.We present a technique to reduce speckle in visible-light optical coherence tomography (vis-OCT) that preserves fine structural details and is robust against sample motion. Specifically, we locally modulate B-scans orthogonally to their axis of acquisition. Such modulation enables acquisition of uncorrelated speckle patterns from similar anatomical locations, which can be averaged to reduce speckle. To verify the effectiveness of speckle reduction, we performed in-vivo retinal imaging using modulated raster and circular scans in both mice and humans. We compared speckle-reduced vis-OCT images with the images acquired with unmodulated B-scans from the same anatomical locations. We compared contrast-to-noise ratio (CNR) and equivalent number of looks (ENL) to quantify the image quality enhancement. Speckle-reduced images showed up to a 2.35-dB improvement in CNR and up to a 3.1-fold improvement in ENL with more discernable anatomical features using eight modulated A-line averages at a 25-kHz A-line rate.

Effect Analysis and Spectral Weighting Optimization of Sidelobe Reduction on SAR Image Understanding

This paper focuses on effect analysis and spectral weighting optimization of sidelobe reduction on synthetic aperture radar (SAR) image understanding. First, a statistical model for each pixel in complex-amplitude distribution of the SAR image is derived to investigate how the sidelobes of the nearby scatterers change the speckle noise. Second, the phase error is analyzed for interferometry applications when high sidelobe is in the presence. The potential benefit of using a sidelobe reduction method for improving interferometric phase accuracy is also investigated. Third, to better understand how the sidelobes affect the quality of SAR images at different sidelobe levels, with the same imaging resolution, one novel type of weights is optimized to improve the performance of spectral weighting for SAR sidelobe reduction by using one bioinspired method. Intensive sidelobe reduction experiments are carried out, and the results are verified on the probability density function (PDF), the level of speckle noise, and interferometric phase accuracy of the SAR images. The experimental results indicate that the sidelobe reduction methods can change the speckle noise and the statistical distributions for the underlying SAR imagery. One of the interesting findings is that a better equivalent number of looks (ENL) and interferometric phase accuracy can be obtained by using the optimized spectral weighting. However, the spatially variant apodization damages the PDFs of the SAR data, decreases the ENL of the SAR images, and leads to significant phase distortion for SAR interferometry.

Ghost imaging under low-rank constraint.

Rather than the commonly used sparsity constraint, a new assumption taking advantage of regularity between rows or columns of a two-dimensional image is introduced to ghost imaging using compressive sensing, namely, low-rank constraint. Both simulation and experiment suggest explicit improvement on image quality of ghost imaging under low-rank constraint over ghost imaging under sparsity constraint (GISC), especially for under-sampling cases, and being in particular advantageous on image smoothness assessed by equivalent numbers of looks. Robustness of low-rank parameter setting is demonstrated. Low-rank constraint is also shown to be a powerful reference-less image quality enhancement tool for images restored by GISC.

Parameter Estimation of Multilook Polarimetric SAR Data Based on Fractional Determinant Moments

This letter presents a novel method to estimate the equivalent number of looks (ENLs) and the texture parameters when multilook polarimetric SAR data are stochastically modeled by the product model. The method is based on the fractional moments of the determinant of the multilook polarimetric covariance (FMDC) matrix. The estimator of ENL is independent of the distribution of the texture model, and the estimator of the texture parameters can be derived for all commonly used multilook PolSAR distributions. K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> is given in this letter as multilook PolSAR distribution. The performance of the FMDC estimator is compared to the performance of other known estimators using simulated and real data.

A clustering framework based on FCM and texture features for denoising ESPI fringe patterns with variable density

Denoising of electronic speckle pattern interferometry (ESPI) fringe patterns with variable density is challenging. In this paper, we present a method to denoise ESPI fringe patterns with variable density by constructing a clustering framework. The framework first clusters the variable density fringes into low density fringes and high density fringes according to the texture features of fringes by using fuzzy c-means (FCM) clustering algorithm. The texture features include energy, contrast, homogeneity and correlation, which can characterize the densities of fringes. Then, the effective existing methods are adopted to denoise the low and high density fringes individually. Finally, the filtered low and high density fringes are combined to obtain the final denoising results of the fringe patterns with variable density. With the help of proposed framework, variable density fringes can be clustered into low and high density fringes without calculating the density map of fringe pattern. Furthermore, the filtering strength applied in high density fringes can be different from that in low density fringes in a fringe pattern, which can improve the performance of any filtering method in the denosing of fringe patterns with variable density. Experiments on computer-simulated and experimentally obtained ESPI fringe patterns tested with several filtering methods show that the proposed framework provides consistently better results than denoising the image directly, in terms of signal-to-noise ratio, equivalent number of looks and edge preservation metrics.

Maximum-Likelihood Parameter Estimation of the Product Model for Multilook Polarimetric SAR Data

The product model is assumed to be an appropriate statistical model for multilook polarimetric synthetic radar data (PolSAR). According to this model, the observed signal is considered as the product of independent random variates of a complex Gaussian speckle and a non-Gaussian texture. With different texture distributions, the product model leads to different expressions for the compound distribution considered as an infinite mixture model. In this paper, the maximum-likeli-hood (ML) estimator is derived to jointly estimate the speckle and texture parameters in the compound distribution model using the multilook polarimetric radar data. In particular, we estimate: 1) the equivalent number of looks; 2) the covariance matrix of the speckle component; and 3) the texture distribution parameters. The expectation–maximization algorithm is developed to compute the ML estimates of the unknown parameters. The hybrid Cramer–Rao bounds (HCRBs) are also derived for these parameters. First, a general HCRB expression is derived under an arbitrary texture distribution. Then, this expression is simplified for a specific texture distribution. The performance of the ML is compared with the performance of other known estimators using the simulated and real multilook PolSAR data. For real data, a goodness of fit of multilook PolSAR data histograms is used to assess the fitting accuracy of the compound distributions using different estimators.

Bandlets on Oriented Graphs: Application to Medical Image Enhancement

In this paper, we introduce a new image modeling method by getting benefit from both sparsity and multiscale characteristics of transform-domain modeling, along with the geometrical representation of the graph-based models. The proposed method is named bandlet on an oriented graph (BOG) and improves directional selectivity property of the bandlets. The conventional wavelet in the bandlet design is substituted with a new non-orthogonal wavelet. The replaced wavelet is defined on a graph. In order to adjust the orientation of the wavelet atoms with the corresponding edges in the image pixels, a directed graph is constructed. The resultant wavelets in discrete scales can be considered as a frame and are created to build a tight frame. To show the effectiveness of this new atomic representation, we demonstrated the performance of the new model in noise alleviation of the optical coherence tomography (OCT) images (from the retina) and microscopic images. Denoising results on OCT are reported on 72 slices, selected arbitrarily out of OCT dataset from Topcon device. The combined method provided an enhancement of contrast to noise ratio (CNR) (from 27.82 to 30.11), and improvement of the equivalent number of looks (ENL) (from 2183.26 to 2217.37) over the state-of-the-art in OCT noise reduction. In the denoising of microscopic images, PSNR improvement (from 26.33 to 35.24) over the original image is shown along with the improvement in next steps of feature extraction.

CFAR-Based Adaptive PolSAR Speckle Filter

The patch-based polarimetric synthetic aperture radar (PolSAR) nonlocal means (NLM) speckle filters are efficacious in noise suppression and detail preservation, but are computationally inefficient. The objective of this paper is to develop a filter that provides better noise suppression and edge preservation along with reduced computational complexity for PolSAR applications. In this paper, the patch-based NLM adaptive speckle filter based on constant false alarm rate (CFAR) edge detector is proposed. The CFAR-based edge detector is used to generate a map that classifies the data into three regions: homogeneous, heterogeneous, and strong edge dominant regions. The proposed speckle filter adapts itself suitably based on the heterogeneity of the region using classified regions as a mask. The performance of proposed filtering technique is evaluated on 1-look simulated (generated by Monte Carlo simulation), 1-look RADARSAT-2, and 4-look AIRSAR data. The performance evaluation is done based on the extent of noise reduction measured by equivalent number of looks, edge preservation degree, bias in estimation, polarimetric structure preservation, and visual appearance. The performance of the proposed filtering technique is found to be better than the state-of-the-art speckle filtering techniques like refined Lee, NLM pretest, and NL-SAR. The order of computational complexity of the proposed filter is found to be better than the pretest or NL-SAR filters.

Speckle Noise Suppression in SAR Images Using a Three-Step Algorithm.

Speckle noise can reduce the image quality of synthetic aperture radar (SAR) and complicate image interpretation. This study proposes a novel three-step approach based on the conventional probabilistic patch-based (PPB) algorithm to minimize the impact of bright structures on speckle suppression. The first step improves the calculation accuracy of the weight by pre-processing speckle noise with a linear minimum mean-square error filter and reassessing similarity between pixels. In the second step, an iterative method is developed to avoid interfering with bright structures and acquires a more accurate homogeneous factor by adaptively changing the size of the search window. In the final step, the spreading and blurring of bright structures is corrected using a modified bias-reduction technique. Experimental results demonstrate the proposed algorithm has improved performance for both speckle suppression and preservation of edges and textures, evaluated by indicators including the equivalent number of looks, the edge preservation index, the mean, and standard deviation of ratio images.

ENHANCEMENT OF BILE DUCT ULTRASOUND IMAGES FOR CHOLANGIOCARCINOMA SURVEILLANCE

Bile Ducts Cancer (Cholangiocarcinoma: CCA) is a serious public health problem. The surgical resection is one of the curative treatments for CCA. The surgical resection in the early stage of the CCA gives a good chance for recovery; therefore, early detection in the surgical resection stage is of a critical importance. Periductal fibrosis (PDF) in the bile duct is one factor which is considered about CCA in Thailand. It can be detected using the liver ultrasound images; however, the liver ultrasound images contain speckle noise which decreases PDF detection performance. This paper proposes a new method for enhancement of bile duct ultrasound image (EBDU). The main idea of the EBDU method is to reduce the speckle noise and enhance clear structures of the liver ultrasound images. The proposed method consists of three processes which are the speckle reduction, edge preservation, and intraregion smoothing. The speckle reduction is performed in frequency domain to filter out the high frequency and low frequency noise out of the ultrasound image by using the bandpass filter. The criterion of the bandpass filter is the size of the structures in the ultrasound image, which could be large structures (mirrored edges) and small structures (smoothing). Edge preservation is then performed by applying the statistics filter to distinctly increase the boundary structure; it sorts out all pixel values from the surrounding neighborhood into a numerical order. Finally, intraregions of the liver ultrasound images are smoothed by intra-region smoothing process which can diffuse the homogeneous region. The enhanced liver ultrasound images were tested by computer-aided detection on PDF regions (CADPDF). Variety of liver ultrasound images were used. To evaluate and compare the performance of the proposed method, four measurements are applied which are the scan line pixel (SLP), the Slope-Smooth Speckle Index (SLSI), Equivalent number of looks (ENL), Speckle suppression index (SSI), and the accuracy achieved through the PDF regions segmentation. The experimental results show that the proposed method is able to give higher success rate of enhancing liver ultrasound images compared to other methods. Moreover, the accuracy of CADPDF system was evaluated. The proposed EBDU method and other methods are applied in CADPDF system. The experimental results show that the proposed EBDU method achieved better performance than other methods, giving an accuracy of 80.36%.

PCANet based nonlocal means method for speckle noise removal in ultrasound images.

Speckle reduction remains a critical issue for ultrasound image processing and analysis. The nonlocal means (NLM) filter has recently attached much attention due to its competitive despeckling performance. However, the existing NLM methods usually determine the similarity between two patches by directly utilizing the gray-level information of the noisy image, which renders it difficult to represent the structural similarity of ultrasound images effectively. To address this problem, the NLM method based on the simple deep learning baseline named PCANet is proposed by introducing the intrinsic features of image patches extracted by this network rather than the pixel intensities into the pixel similarity computation. In this approach, the improved two-stage PCANet is proposed by using Parametric Rectified Linear Unit (PReLU) activation function instead of the binary hashing and block histograms in the original PCANet. This model is firstly trained on the ultrasound database to learn the convolution kernels. Then, the trained PCANet is utilized to extract the intrinsic features from the image patches in the pre-denoised version of the noisy image to be despeckled. These obtained features are concatenated together to determine the structural similarity between image patches in the NLM method, based on which the weighted mean of all pixels in a search window is computed to produce the final despeckled image. Extensive experiments have been conducted on a variety of images to demonstrate the superiority of the proposed method over several well-known despeckling algorithm and the PCANet based NLM method using ReLU function and sigmoid function. Visual inspection indicates that the proposed method outperforms the compared methods in reducing speckle noise and preserving image details. The quantitative comparisons show that among all the evaluated methods, our method produces the best structural similarity index metrics (SSIM) values for the synthetic image, as well as the highest equivalent number of looks (ENL) value for the simulated image and the clinical ultrasound images.

Assessment of a Power Law Relationship Between P-Band SAR Backscatter and Aboveground Biomass and Its Implications for BIOMASS Mission Performance

This paper presents an analysis of a logarithmic relationship between P-band cross-polarized backscatter from synthetic aperture radar (SAR) and aboveground biomass (AGB) across different forest types based on multiple airborne datasets. It is found that the logarithmic function provides a statistically significant fit to the observed relationship between HV backscatter and AGB. While the coefficient of determination varies between datasets, the slopes, and intercepts of many of the models are not significantly different, especially when similar AGB ranges are assessed. Pooled boreal and pooled tropical data have slopes that are not significantly different, but they have different intercepts. Using the power law formulation of the logarithmic relation allows estimation of both the equivalent number of looks (ENL) needed to retrieve AGB with a given uncertainty and the sensitivity of the AGB inversion. The campaign data indicates that boreal forests require a larger ENL than tropical forests to achieve a specified relative accuracy. The ENL can be increased by multichannel filtering, but ascending and descending images will need to be combined to meet the performance requirements of the BIOMASS mission. The analysis also indicates that the relative change in AGB associated with a given backscatter change depends only on the magnitude of the change and the exponent of the power law, and further implies that to achieve a relative AGB accuracy of 20% or better, residual errors from radiometric distortions produced by the system and environmental effects must not exceed 0.43 dB in tropical and 0.39 dB in boreal forests.

A Deception Jamming Method Countering Bi- and Multistatic ISAR Based on Micro-Doppler Effect

Bi- and multistatic inverse synthetic aperture radar (ISAR) operate with spatially separated transmitting and receiving antennas. A deception jamming method countering bi- and multistatic ISAR is proposed in this paper based on the study of micro-Doppler effect. The jammer modulates the intercepted ISAR signals with added micro-Doppler information and retransmits them to the real target, which scatters the jamming signals to the radar receivers. Deceptive false-target images with interference bands in the cross-range direction will be induced by the jamming signals through the imaging process of radar receivers. Additionally, real-time movement features of the false-targets can be flexibly adjusted by changing the modulation parameters, which improves the fidelity of the false-targets. The equivalent number of looks (ENL) index is used to evaluate the jamming effects. Simulation results validate our theoretical analysis and show the effectiveness and practicability of our method.

A Novel Edge Preserving Improved Adaptive Wavelet Filter (EPIAWF) for Speckle Removal in Ultrasound Images

Background: The paper proposes a new method for improving the quality of ultrasound medical images using a two stage wavelet based denoising approach. The first stage utilizes an adaptive wavelet shrinkage function with a novel interscale measure based variance estimate and a new subband adaptive threshold for reducing the speckle present in ultrasound images. Methods: The performance of the first stage is further improved in the second by employing an error measure estimate. Thus the proposed denoising approach is optimized in the mean square error sense and results in improved noise removal as well as in the edge preservation of the reconstructed image. It also improves the visual quality of the ultrasound images. Next, quantitative evaluation is also done with performance metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM), Equivalent Number of Looks (ENL) and Edge preservation Index (EPI). Results: The performance of the proposed approach is measured and compared with the existing approaches and found to be better in improving the quality of ultrasound images. Keywords: Ultrasound images, speckle, adaptive filter, undecimated, wavelet, edge preservation.

A Preliminary Evaluation of the GaoFen-3 SAR Radiation Characteristics in Land Surface and Compared With Radarsat-2 and Sentinel-1A

The first evaluation of GaoFen-3 SAR radiation characteristics and its potential for quantitative parameter estimation in land surface is presented in this letter. Based on two triangle corner reflectors, five impulse response property parameters are calculated to assess image quality of GaoFen-3 SAR, and results show that the peak sidelobe ratio is superior to system specification and the integrated sidelobe ratio cannot be assessed by misconduct in the field campaign mainly for strong background scattering (not low enough). Five distributed target parameters are extracted from three categories and compared with Radarsat-2 and Sentinel-1A quasi-synchronous images in two days. The results indicate good radiometric resolution (RR) of 3 dB and good equivalent number of looks closed to 1 for GaoFen-3 single-look complex product, which proves GaoFen-3 SAR image is at the same quality level with the other two C-band SAR images and its RR meets system design of 3.5 dB. Then, the backscatter coefficient of these three SAR images is compared at bare soil area after GaoFen-3’s incidence angle being normalized to 43° by Oh2004 empirical model, and the results reveal the problem of several decibels lower for GaoFen-3 in absolute radiometric calibration. Finally, the capability of surface parameter estimation is justified by statistical relation of GaoFen-3 co-polarization backscatter coefficient and bare soil moisture content, indicating its good potential for quantitative applications in land after solving the problem in calibration.

Iterative adaptive Despeckling SAR image using anisotropic diffusion filter and Bayesian estimation denoising in wavelet domain

In this paper, a new iterative algorithm has been presented by aggregating Stationary Wavelet Transform (SWT), Bilateral filtering, Bayesian estimation, and Anisotropic Diffusion (AD) filtering to reduce the speckle noise in SAR images. For this purpose, speckle images were first decomposed using two-dimensional stationary wavelet transform and then a suitable filtering method was used to filter respective coefficients of each sub-band of the speckled images. Generally, in wavelet transform-based noise reduction methods, filtering and thresholding techniques are usually applied to the coefficients of the detail sub-bands and the residual speckle noise is ignored in the approximate sub-band. In this paper, bilateral filtering has been applied to reduce the speckle noise in the approximate sub-band. We used Bayesian estimator to calculate the noise-free signal in the horizontal and vertical sub-bands with respect to that some parts of signal coefficients are eliminated in the traditional thresholding techniques. Moreover, we applied anisotropic diffusion filtering method to preserve the edges and structure of image along the diagonal subband which has more details (the entropy is maximum) than other directions in radar and optic images. Finally, both the proposed algorithm and other speckle noise reduction methods were applied on two synthetic speckled images and an actual SAR image in San Francisco. Their efficiencies were compared according to the Structural SIMilarity(SSIM), Peak Signal to Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Speckle Suppression Index (SSI) and Speckle Suppression and Mean Preservation Index (SMPI). The experimental results indicate that the proposed algorithm efficiently reduces the speckle noise and preserves the edges and structure of image.

A new SAR image despeckling using directional smoothing filter and method noise thresholding

The removal of granular pattern multiplicative speckle noise is the major issue in Synthetic Aperture Radar (SAR) images. Theoretically, multiplicative noise is considered as the ratio of the standard deviation to the signal value. The proposed scheme works on db2 based 2D-Discrete Wavelet Transform (DWT) using wavelet thresholding and method noise. The scheme is designed to despeckle the simulated SAR images and real SAR images. It uses a hybrid combination of Directional Smoothing Filter (DSF), wavelet thresholding using enhanced Bayesian shrinkage rule and method noise for despeckling. After DWT decomposition, the approximate component is directed to DSF followed by method noise thresholding and detailed components are subjected to wavelet thresholding followed by method noise thresholding. The article justifies the efficient use of method noise and explains how its exact use can enhance the result of the algorithm over other efficient methods. The quality assessment of the proposed method is done by visual appearance and measures such as Peak Signal-to-Noise Ratio (PSNR), Structural similarity index metric (SSIM), Universal Image Quality Index (UIQI), Equivalent Numbers of Looks (ENL), Noise Variance (NV), Coefficient of Variation (CV), Mean Squared Error (MSE), Correlation Coefficient (CC) and preservation of mean values before and after despeckling. The effectiveness of the proposed method is demonstrated by matching it to well-known speckle noise removal methods on SAR images. The proposed method has the capability to be used in real time practical applications.

A maximum likelihood filter using non-local information for despeckling of ultrasound images

This work presents a new ultrasound image despeckling method based on the maximum likelihood principle that effectively exploits non-local information for estimating noise-free pixels. First, a new maximum likelihood filter is proposed which uses neighborhood information to despeckle images. For this purpose, the general speckle model is used in the log-likelihood function and despeckled pixels are obtained by maximizing this function. Second, the proposed filter is developed to use non-local information such that the distribution of each noisy pixel is weighted according to the statistical distance between the patch of the noisy pixel and that of the target pixel. Because it is optimally designed for ultrasound images, the Pearson distance is used to measure the statistical distance between the patches. A series of experiments are conducted on three different ultrasound images and one synthetic image. Subjective evaluations show that the proposed method is able to preserve edges and structural details of the image and objective evaluations using equivalent number of looks, natural image quality evaluator, peak signal-to-noise ratio, mean preservation, and structural similarity confirm that the proposed method can achieve superior performance.

A Hybrid Method of SAR Speckle Reduction Based on Geometric-Structural Block and Adaptive Neighborhood

Given the improvement of synthetic aperture radar (SAR) imaging technologies, the resolution of SAR image is largely improved and the variation of backscatter amplitude should be considered in SAR image processing. In this paper, considering the spatial geometric properties of SAR image in gray pixel space and the sample selection in the estimation of true signal, local directional property of each pixel is explored with the help of SAR sketching method, and two specially designed filters are integrated for adaptive speckle reduction of SAR images. Specifically, based on the sketch map of a SAR image, the orientation of the sketch point lying at each sketch segment is assigned to the corresponding pixel, and thus all pixels of the SAR image are classified as the directional pixels and the nondirectional pixels. For the directional pixels, given the significant directionality of its neighborhood, a geometric-structural block (GB) is built to center on it and GB-wised nonlocal means filter is designed to estimate the true values of all pixels contained in the GB. Moreover, using the local orientation, the whole image is adopted as the searching range to search the similar GBs. For the nondirectional pixels, based on the locally estimated equivalent number of looks, a novel pixel-based metric is proposed to determine the local adaptive neighborhood (AN) with which an AN-based filter is developed to estimate its true value. Besides, since some nondirectional pixels are contained in GBs, a Bayesian-based fusion strategy is designed for the fusion of their estimated values. In the experiments, three synthetic speckled images and five real SAR images [obtained with different resolutions (e.g., 3, 1, and 0.1 m) and different bands (e.g., X-band, C-band, and Ka-band)] are used for evaluation and analysis. Owing to the usage of local spatial geometric property and the combination of two different filters, the proposed method shows a reasonable performance among the comparison methods, in terms of the speckle reduction and the details’ preservation.