Equivalent Number Of Looks Research Articles

Focused Synthetic Aperture Radar Processing of Ice-Sounding Data Collected Over the East Antarctic Ice Sheet via the Modified Range Migration Algorithm Using Curvelets

In this paper, we propose a new algorithm to address the speckle noise problem in imaging of ice sheets. It is a wave-equation-based ice-sounding imaging method using curvelets as building blocks of ice-sounding data, which successfully images the topography of ice sheets. First, theory analysis has been carried on to the proposed algorithm. Then, we give the specific steps to implement this algorithm. Finally, we apply this algorithm to the simulation point targets and High-Resolution Ice-Sounding Radar data to prove its validity of imaging of ice sheets. Furthermore, compared with five previous methods in two major aspects—the power of clutter reduction and the equivalent number of looks—the proposed algorithm reduces the speckle noise during the imaging processing without degrading the ability in clutter reduction.

An Affinity-Based Algorithm in Nonsubsampled Contourlet Transform Domain: Application to Synthetic Aperture Radar Image Denoising

An algorithm in which affinity is merged into nonsubsampled contourlet transform (NSCT) to denoise synthetic aperture radar (SAR) image is proposed. The important information (boundaries or details) or non-important information (homogeneous area) in a SAR image is estimated based on affinity matrix, by which the affinity-based denoising assigns a high affinity element to the coefficients of NSCT that belong to same region. The foreground and background in NSCT domain are automatically initialized which avoids the need for user initialization. Foreground probability obtained by optimizing objective function can be used to achieve the posterior ratio. Combining the posterior ratio and prior ratio, we can obtain the shrunk coefficients. The proposed algorithm was applied to real SAR images denoising and compared through the SAR image vision effect, the equivalent number of looks (ENL) and the edge sustain index (ESI). Experimental results show that the proposed algorithm outperforms the compared algorithms and achieves the better denoising result and edge preservation.

Speckle reduction in optical coherence tomography by adaptive total variation method

An adaptive total variation method based on the combination of speckle statistics and total variation restoration is proposed and developed for reducing speckle noise in optical coherence tomography (OCT) images. The statistical distribution of the speckle noise in OCT image is investigated and measured. With the measured parameters such as the mean value and variance of the speckle noise, the OCT image is restored by the adaptive total variation restoration method. The adaptive total variation restoration algorithm was applied to the OCT images of a volunteer’s hand skin, which showed effective speckle noise reduction and image quality improvement. For image quality comparison, the commonly used median filtering method was also applied to the same images to reduce the speckle noise. The measured results demonstrate the superior performance of the adaptive total variation restoration method in terms of image signal-to-noise ratio, equivalent number of looks, contrast-to-noise ratio, and mean square error.

Unbiased-average minimum biased diffusion speckle denoising approach for synthetic aperture radar images

Means of synthetic aperture radar (SAR) images represent the radiation densities of scenes, and the preservation of means is significant in speckle denoising for the application of SAR images. We provide an improved scheme of the minimum biased diffusion (MinBAD) algorithm for speckle denoising using partial differential equations. Considering the characteristics of SAR speckle and the radiation accuracy for postprocessing needs, several improvements such as normalization, homomorphic transformation, and average-preserving processing are introduced into the MinBAD algorithm. Besides the equivalent number of looks and edge preserving index, a new index, radiation accuracy error, is defined to evaluate the denoising effect. Experimental results for both artificial images and real SAR images are used to validate the performance of the proposed unbiased-average MinBAD speckle reducing approach.

Effective speckle noise suppression in optical coherence tomography images using nonlocal means denoising filter with double Gaussian anisotropic kernels

Non-local means (NLM) filter is one of the state-of-the-art denoising filters. It exploits the presence of similar features in an image and averages those similar features to remove noise. However, a conventional NLM filter shows somewhat inferior performance of noise reduction around edges, suffering from low efficiency of collecting similar features to be averaged. In order to overcome this phenomenon, we propose a NLM filter with double Gaussian anisotropic kernels as a substitute for the conventional homogeneous kernel to effectively remove noise from OCT images corrupted by speckle noise. The proposed filter was evaluated by comparing with various denoising filters such as conventional NLM filter, median filter, bilateral filter, and Wiener filter. The fingertip OCT images, which were processed with the different denoising filters, indicated that the proposed NLM filter provides superior denoising performance, among the filters in terms of the contrast-to-noise ratio (CNR), the equivalent number of looks (ENL), and the speckle suppression index (SSI). A human retina OCT image was also used to compare and show the performances of noise reduction among different filters. In addition, the denoising performance with the proposed NLM filter was also investigated in the synthetic images for fair comparison among the filters by calculating the peak signal-to-noise ratio (PSNR). The proposed NLM filter outperformed the conventional NLM filter as well as the other filters.

Patch Ordering-Based SAR Image Despeckling Via Transform-Domain Filtering

In this paper, we propose a synthetic aperture radar (SAR) image despeckling method based on patch ordering and transform-domain filtering. Logarithmic transformation with bias correction is applied to the original SAR image to transform the multiplicative noise model into the additive model. Then, we adopt a two-stage filtering strategy. The first stage is coarse filtering which can suppress speckle effectively. In this stage, we extract the sliding patches from the logarithmic SAR image, and order them in a smooth way by a simplified patch ordering algorithm specially for SAR images. The ordered patches are filtered by learned simultaneous sparse coding (SSC), a technology recently advanced in image processing. Then, the coarse filtering result is reconstructed from the filtered patches via inverse permutation and subimage averaging. The second stage is refined filtering which can eliminate small artifacts generated by the coarse filtering. In this stage, the sliding patches are extracted from the coarse filtering result and ordered in the same way. Then, we apply 2-D wavelet hard-thresholding to the ordered patches and reconstruct the refined filtering result. The final result is obtained by taking exponential transformation to the refined filtering result. An algorithm based on the proposed strategy is presented in detail and the parameters are selected for fast and effective realization. Experimental results with both simulated images and real SAR images demonstrate that the proposed method achieves state-of-the-art despeckling performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, equivalent number of looks (ENLs), and ratio image.

Bayesian denoising of ultrasound images using heavy‐tailed Levy distribution

Ultrasound images often exhibit poor signal to noise ratio when compared with optical images, because of the presence multiplicative speckle noise. Speckle suppression is often carried out as a pre-processing step to aid in diagnosis using ultrasound images. In this study, dual tree complex wavelet transform-based Levy Shrink algorithm is proposed for denoising ultrasound images. The coefficients in each wavelet subband are modelled using a heavy tailed Levy distribution. The scale parameters of the Levy distribution are estimated using fractional moments. Within this framework, a Bayesian estimator is employed to denoise ultrasound images. The proposed Levy Shrink algorithm is verified using evaluation parameters such as peak signal to noise ratio, mean structural similarity index, correlation coefficient and equivalent number of looks. The efficiency of the proposed denoising algorithm is justified by conducting extensive experiments on real as well as simulated ultrasound images.

Comparing Nonsubsampled Wavelet, Contourlet and Shearlet Transforms for Ultrasound Image Despeckling

Abstract —Ultrasound images suffer of multiplicative noise named speckle. Bayesian shrinkage in transform domain is a well-known method based on finding threshold value to suppress the speckle noise. The main problem of applying Bayesian shrinkage is finding the optimum threshold value in appropriate transform domain. In this paper, we compare the performance of adaptive Bayesian thresholding when nonsubsampled Wavelet, Contourlet and Shearlet transforms are used. We processed two synthetic test images and three original ultrasound images as well to demonstrate the efficiency of the designed filters. In order to compare the performance of Bayesian shrinkage when employing the three mentioned transform domain, we used peak signal to noise ratio (PSNR), mean square error (MSE), and structural similarity (SSIM) as the full-reference (FR) objective criteria parameters and noise variance (NV), mean square difference (MSD), and equivalent number of looks (ENL) as the no-reference (NR) objective criteria parameters.

Two-Dimensional Imaging of Ice Sheets of Airborne Radar Sounder via a Combined Modified Range Migration Algorithm Based on ISFT and Beamforming Using Curvelets

We develop and demonstrate a new method to address the surface clutter problem in imaging of ice sheets by airborne radar sounder. This new method utilizes the modified range migration algorithm based on inverse scaled Fourier transform (ISFT) to reduce along-track clutter and the beamforming using curvelets to reduce cross-track clutter. First, theory analysis has been carried on to the proposed method. Then, we give the particular realizing steps to implement this method. At last, we apply this method to the simulation point targets and MCoRDS data to prove its validity of imaging of ice sheets. Furthermore, we show the advantages of the proposed method through comparison with two methods in four major aspects—the power of along-track clutter reduction, the power of cross-track clutter reduction, the equivalent number of looks (ENLs), and the calculating time.

TerraSAR反射強度画像のスペックルノイズの低減効果の評価

In this study, the effect of speckle noise reduction was evaluated with high resolution satellite TerraSAR intensity data. Multi-temporal polarization imagery (HH & VV) was acquired on different growth stages of rice plant. Three kinds of filters, i.e. Lee, Frost, Gamma Map, were applied for speckle noise filtering with different window sizes. Six indices, i.e. Standard deviation, Equivalent number of looks, Speckle suppression index, Edge preservation index, Image sharpness, and Image detail preservation index, were used in order to compare and assess the effect of speckle noise reduction of different filters. Based on the evaluation results, the most appropriate window size was determined for speckle noise filtering of utilized data.

A new patch based change detector for polarimetric SAR data

In this paper, a patch based change detection method for polarimetric synthetic aperture radar (PolSAR) data is proposed. Because of the inherent speckle interference in SAR system, a despeckling procedure is important for change detection. However, the residual speckle still exists in the filtered data and is unevenly distributed, which is not considered in the available change detectors. Besides that, the detection methods with despeckling procedure cannot be adjusted based on the constant false alarm rate (CFAR). Specifically, the available PolSAR change detection methods are inefficient for the strong speckle interference cases due to the absence of the despeckling procedure. In order to overcome these drawbacks, an improved change detection framework for PolSAR data is proposed in this paper. The main innovations in the new framework include: (1) a despeckling procedure is added for PolSAR change detection, which makes the algorithm robust to strong noise; (2) a new equivalent number of looks (ENL) estimator is proposed for the PolSAR/SAR data filtered by non-local means filter; (3) the proposed method which detects the changes based on the filtered data can be adjusted with different CFARs. The experiments on both synthetic and real PolSAR datasets have demonstrated that the effectiveness of the proposed patch based PolSAR change detector (PPCD).

Measurements of Forest Biomass Change Using P-Band Synthetic Aperture Radar Backscatter

Methods to estimate forest biomass change have been investigated using experimental P-band synthetic aperture radar (SAR) data from the recent airborne campaigns BioSAR 2007 and BioSAR 2010 conducted over a hemiboreal test site in southern Sweden. Regression models based on backscatter change were developed using reference biomass change maps derived from high-density laser scanning data. Different regression models were developed for linear, square root, and logarithmic biomass change scales. The models were compared to the change maps based on laser data using twofold cross-validation, and estimation errors were evaluated using six 80 m by 80 m plots with detailed in situ measurements. The results indicate that the root-mean-square error of biomass change estimates based on P-band SAR backscatter data is about 15% or 20 t/ha. This suggests that not only clear-cuts but also growth and thinning can be measured. Simulations were performed in order to evaluate the possibility of using a spaceborne P-band SAR for measurements of forest biomass change. The simulations show that, with 64 equivalent number of looks (ENL) and a 50% change in biomass, it is possible to correctly indicate whether the forest has gained or lost biomass. Similarly, for a biomass loss of more than 75%, a correct indication of the sign of biomass change can be achieved with only 8 ENL.

Study on Synthetic Aperture Radar Image Denoising Algorithm

In synthetic aperture radar (SAR) inherent speckle will affect the legibility of image details; give the image target detection adverse effects. In order to reduce the SAR image speckles noise, this article provided an improved algorithm based on median filter and wavelet semi-soft threshold shrinkage. First, reduced the SAR image speckle with median filtering method, then, with the filtered image, filtering the image with wavelet half soft threshold value contraction algorithm to noise, Simulation results show that the algorithm based on median filtering and improved algorithm of wavelet half soft threshold shrinkage of SAR image can better remove the speckle noise of the SAR image, while keep better edge, in Equivalent Number of Looks (ENL) and edge keep ability (FOM) aspects, it would be better than median filtering.

Bias Correction and Modified Profile Likelihood Under the Wishart Complex Distribution

This paper proposes improved methods for the maximum likelihood (ML) estimation of the equivalent number of looks $L$. This parameter has a meaningful interpretation in the context of polarimetric synthetic aperture radar (PolSAR) images. Due to the presence of coherent illumination in their processing, PolSAR systems generate images which present a granular noise called speckle. As a potential solution for reducing such interference, the parameter $L$ controls the signal-noise ratio. Thus, the proposal of efficient estimation methodologies for $L$ has been sought. To that end, we consider firstly that a PolSAR image is well described by the scaled complex Wishart distribution. In recent years, Anfinsen et al. derived and analyzed estimation methods based on the ML and on trace statistical moments for obtaining the parameter $L$ of the unscaled version of such probability law. This paper generalizes that approach. We present the second-order bias expression proposed by Cox and Snell for the ML estimator of this parameter. Moreover, the formula of the profile likelihood modified by Barndorff-Nielsen in terms of $L$ is discussed. Such derivations yield two new ML estimators for the parameter $L$, which are compared to the estimators proposed by Anfinsen et al. The performance of these estimators is assessed by means of Monte Carlo experiments, adopting three statistical measures as comparison criterion: the mean square error, the bias, and the coefficient of variation. Equivalently to the simulation study, an application to actual PolSAR data concludes that the proposed estimators outperform all the others in homogeneous scenarios.

Random transverse motion-induced spatial compounding for fiber bundle imaging.

We propose and demonstrate a novel fiber bundle imaging based on spatial compounding induced by random transverse motion to remove the pixelation effect, to improve resolution, and to increase image quality. The experimental results using a USAF target and pyramidal neuron cell showed that 20-frame compounding improved image quality (contrast-to-noise ratio by >9 dB, global SNR by >6 dB, equivalent number of looks by >1.8 times, and 1/β by >1.5 times), resolution by better than 2μm, and completely eliminated pixelation artifact.

An improved adaptive wavelet shrinkage for ultrasound despeckling

Ultrasound imaging is the most widely used medical diagnostic technique for clinical decision making, due to its ability to make real time imaging for moving structures, low cost and safety. However, its usefulness is degraded by the presence of signal dependent speckle noise. Several wavelet-based denoising schemes have been reported in the literature for the removal of speckle noise. This study proposes a new and improved adaptive wavelet shrinkage in the translational invariant domain. It exploits the knowledge of the correlation of the wavelet coefficients within and across the resolution scales. A preliminary coefficient classification representing useful image information and noise is performed with a novel inter-scale dependency measure. The spatial context adaptation of the wavelet coefficients within a subband is achieved by a local spatial adaptivity indicator, determined by using a truncation threshold. A weighted signal variance is estimated based on this measure and used in the determination of a subband adaptive threshold. The proposed thresholding function aims to reduce the fixed bias of the soft thresholding approach. Experiments conducted with the proposed filter are compared with the existing filtering algorithms in terms of 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). A comparison of the results shows that the proposed filter achieves an improvement in terms of quantitative measures and in terms of visual quality of the images.

A new metric for measuring structure-preserving capability of despeckling of SAR images

In this paper, a new metric called despeckling structure loss (DSL) is proposed for performance assessment of despeckling algorithms with a focus on the preservation of structural features such as edges and textures. The ratio image of a synthetic aperture radar (SAR) image to its despeckled result can clearly indicate the loss of structural features caused by the despeckling process. By taking into account characteristics of the best and worst structure preservation in despeckling, the DSL metric examines the presence of structural features in ratio images by using local correlations between the ratio image and the noise-free reference image at edge points. The DSL is shown to be a monotonic function of structure loss bounded between the best and worst cases, leading an objective and quantitative measure of the structure-preserving capability of despeckling algorithms. Experimental evaluations of the proposed metric have been carried out on simulated SAR images including one generated by SAR raw signal simulator. Despeckled results using five typical algorithms clearly demonstrate the efficiency of the DSL metric. In comparison, the commonly used despeckling metrics including the signal-to-mean-square error ratio, the edge correlation index, the Pratt’s figure of merit, the structural similarity and the equivalent number of looks of ratio images, fail to keep a consistency with the structure loss shown in despeckled results as well as ratio images.

Texture-Invariant Estimation of Equivalent Number of Looks Based on Trace Moments in Polarimetric Radar Imagery

This letter introduces a novel estimator of equivalent number of looks (ENL) that can be applied to any distribution of texture model, i.e., an estimator that is texture invariant. The novel estimator is the Development of Trace Moments (DTM), which cancels the textural variation using trace moments. Five forms of the DTM estimator using submatrices are presented and compared with each other. The results show that the full-dimensional matrix form seems to be the best in performance and computational complexity. The experiments were performed using simulated and real data. The comparisons among all the existing methods of ENL estimation in the product model of the clutter, such as K-distribution and G0 distribution, show the performance of the DTM estimator to be the best if there is a sufficient number of samples. The global and local ENL estimations of the real data of San Francisco are analyzed, and the results agree with the simulated case. This shows that the DTM always gives a good result, particularly in the global estimation of ENL. Therefore, it can be concluded that the DTM estimator is robust to any distribution model, with low computational complexity and high accuracy, particularly in wide areas with similar scattering mechanism.

Speckle reduction in optical coherence tomography images based on wave atoms.

Optical coherence tomography (OCT) is an emerging noninvasive imaging technique, which is based on low-coherence interferometry. OCT images suffer from speckle noise, which reduces image contrast. A shrinkage filter based on wave atoms transform is proposed for speckle reduction in OCT images. Wave atoms transform is a new multiscale geometric analysis tool that offers sparser expansion and better representation for images containing oscillatory patterns and textures than other traditional transforms, such as wavelet and curvelet transforms. Cycle spinning-based technology is introduced to avoid visual artifacts, such as Gibbs-like phenomenon, and to develop a translation invariant wave atoms denoising scheme. The speckle suppression degree in the denoised images is controlled by an adjustable parameter that determines the threshold in the wave atoms domain. The experimental results show that the proposed method can effectively remove the speckle noise and improve the OCT image quality. The signal-to-noise ratio, contrast-to-noise ratio, average equivalent number of looks, and cross-correlation (XCOR) values are obtained, and the results are also compared with the wavelet and curvelet thresholding techniques.

Unsupervised Estimation of the Equivalent Number of Looks Based on Edge Strength Map in SAR Images

Unsupervised Estimation of the Equivalent Number of Looks Based on Edge Strength Map in SAR Images