Despeckling Methods Research Articles

Study on the Despeckle Methods in Angular Surface Plasmon Resonance Imaging Sensors

The speckle effects caused by the coherence of the laser light sources have restricted the performances of angular surface plasmon resonance (SPR) imaging sensors, which are widely used due to their wide linear rage, high refractive index (RI) resolution, and simple structure. In this work, we experimentally studied the influence of two common despeckle methods (laser with a rotating diffuser and an incoherent light-emitting diodes (LED) light source) on the RI resolution of an angular SPR imaging sensor. We found that the LED light source with a band-pass filter could not only eliminate the speckle effect completely but also provide better RI resolution than the other despeckle method. This low-cost speckle-free method has the potential to promote the development and the industrialization of angular SPR imaging sensors.

SVM-Based CAC System for B-Mode Kidney Ultrasound Images.

The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee's sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.

An effective ultrasound video communication system using despeckle filtering and HEVC.

The recent emergence of the high-efficiency video coding (HEVC) standard promises to deliver significant bitrate savings over current and prior video compression standards, while also supporting higher resolutions that can meet the clinical acquisition spatiotemporal settings. The effective application of HEVC to medical ultrasound necessitates a careful evaluation of strict clinical criteria that guarantee that clinical quality will not be sacrificed in the compression process. Furthermore, the potential use of despeckle filtering prior to compression provides for the possibility of significant additional bitrate savings that have not been previously considered. This paper provides a thorough comparison of the use of MPEG-2, H.263, MPEG-4, H.264/AVC, and HEVC for compressing atherosclerotic plaque ultrasound videos. For the comparisons, we use both subjective and objective criteria based on plaque structure and motion. For comparable clinical video quality, experimental evaluation on ten videos demonstrates that HEVC reduces bitrate requirements by as much as 33.2% compared to H.264/AVC and up to 71% compared to MPEG-2. The use of despeckle filtering prior to compression is also investigated as a method that can reduce bitrate requirements through the removal of higher frequency components without sacrificing clinical quality. Based on the use of three despeckle filtering methods with both H.264/AVC and HEVC, we find that prior filtering can yield additional significant bitrate savings. The best performing despeckle filter (DsFlsmv) achieves bitrate savings of 43.6% and 39.2% compared to standard nonfiltered HEVC and H.264/AVC encoding, respectively.

Combination of geometric clustering and nonlocal means for SAR image despeckling

Recently, nonlocal filtering methods for synthetic aperture radar (SAR) despeckling have attracted a lot of attention. In general, a suitable similarity measure well suited to SAR images is derived by incorporating the noise statistics. One important nonlocal framework is the probabilistic patch-based (PPB) filter, which derives the similarity measure in a data-driven way and provides promising results. A drawback of this filter is the suppression of thin and dark details since the PPB method takes into account the photometrically similar patches, yet it ignores the geometric structure of image patches. To overcome these disadvantages, a new patch-based despeckling method is presented which exploits both geometrical and photometrical similarities. Numerical experiments suggest that the proposed method is on a par with or exceeds the state-of-the-art PPB method, both visually and quantitatively.

Despeckle Filtering for Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Ultrasound Images of the Intima-Media Complex.

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Typically, the IMT grows with age and this is used as a sign of increased risk of CVD. Beyond thickness, there is also clinical interest in identifying how the composition and texture of the intima-media complex (IMC) changed and how these textural changes grow into atherosclerotic plaques that can cause stroke. Clearly though texture analysis of ultrasound images can be greatly affected by speckle noise, our goal here is to develop effective despeckle noise methods that can recover image texture associated with increased rates of atherosclerosis disease. In this study, we perform a comparative evaluation of several despeckle filtering methods, on 100 ultrasound images of the CCA, based on the extracted multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) texture features and visual image quality assessment by two clinical experts. Texture features were extracted from the automatically segmented IMC for three different age groups. The despeckle filters hybrid median and the homogeneous mask area filter showed the best performance by improving the class separation between the three age groups and also yielded significantly improved image quality.

Ultrasound image enhancement using structure-based filtering.

Ultrasound images are prone to speckle noises. Speckles blur features which are essential for diagnosis and assessment. Thus despeckling is a necessity in ultrasound image processing. Linear filters can suppress speckles, but they smooth out features. Median filter based despeckling algorithms produce better results. However, they may produce artifact patterns in the resulted images and oversmooth nonuniform regions. This paper presents an innovative despeckle procedure for ultrasound images. In the proposed method, the diffusion tensor of intensity is computed at each pixel at first. Then the eigensystem of the diffusion tensor is calculated and employed to detect and classify the underlying structure. Based on the classification result, a feasible filter is selected to suppress speckles and enhance features. Test results show that the proposed despeckle method reduces speckles in uniform areas and enhances tissue boundaries and spots.

Ultrasound image anisotropic diffusion de-speckling method based on Mallat-Zhong discrete wavelet transform wavelet

In view of speckle noise in ultrasound image, there are some disadvantages of traditional anisotropic diffusion methods, such as in-sufficient noise suppression and edge details preservation. A de-speckling method based on Mallat-Zhong Discrete Wavelet Transform( MZ-DWT) wavelet was proposed. The method used MZ-DWT wavelet and Expectation Maximization( EM) algorithm as the discrimination factor between homogeneous and edge regions, making it more accurately to control diffusion intensity and rate and achieving the noise suppression and details preservation. The experimental results show that, the proposed algorithm can better de-speckle while preserving image details and the performance of the method is better than the traditional anisotropic diffusion methods.

IMAGE ENHANCEMENT AND SPECKLE REDUCTION OF FULL POLARIMETRIC SAR DATA BY GAUSSIAN MARKOV RANDOM FIELD

Abstract. In recent years, the use of Polarimetric Synthetic Aperture Radar (PolSAR) data in different applications dramatically has been increased. In SAR imagery an interference phenomenon with random behavior exists which is called speckle noise. The interpretation of data encounters some troubles due to the presence of speckle which can be considered as a multiplicative noise affecting all coherent imaging systems. Indeed, speckle degrade radiometric resolution of PolSAR images, therefore it is needful to perform speckle filtering on the SAR data type. Markov Random Field (MRF) has proven to be a powerful method for drawing out eliciting contextual information from remotely sensed images. In the present paper, a probability density function (PDF), which is fitted well with the PolSAR data based on the goodness-of-fit test, is first obtained for the pixel-wise analysis. Then the contextual smoothing is achieved with the MRF method. This novel speckle reduction method combines an advanced statistical distribution with spatial contextual information for PolSAR data. These two parts of information are combined based on weighted summation of pixel-wise and contextual models. This approach not only preserves edge information in the images, but also improves signal-to-noise ratio of the results. The method maintains the mean value of original signal in the homogenous areas and preserves the edges of features in the heterogeneous regions. Experiments on real medium resolution ALOS data from Tehran, and also high resolution full polarimetric SAR data over the Oberpfaffenhofen test-site in Germany, demonstrate the effectiveness of the algorithm compared with well-known despeckling methods.

SAR image despeckling by combining saliency map and threshold selection

Speckle suppression and detail preservation are a unity of contradiction during synthetic aperture radar (SAR) despeckling. In this article, an effective despeckling method is proposed based on a self-adaptive neighbourhood, which takes into account the trade-off between homogeneous region suppression and point target preservation. The size and shape of the neighbourhood depend on an adaptive threshold that is treated as a linear function of a saliency map and a threshold range estimated by the Monte Carlo algorithm based on a given significance level and the number of looks in SAR. If the saliency of the current pixel is large, which always means that the current pixels should belong to a point target, a linear object, or an edge, a relatively large threshold will be assigned to search a neighbourhood with a small size. Conversely, pixels with a small saliency value will obtain a non-local neighbourhood with a large size. A maximum-likelihood estimation is employed to estimate the real radar reflectivity based on the pixels in the adaptive neighbourhood. The comparison experiments on simulated and actual SAR data validate the effectiveness of the proposed algorithm and demonstrate its overall speckle filtering characteristics compared with other algorithms. The visual and numerical experimental results show that the proposed despeckling method provides superior performance compared with several state-of-the-art despeckling methods.

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain methods.

Wavelet based Despeckling of Medical Ultrasound Images using Speckle Reducing Anisotropic Diffusion and Local Wiener Filtering

Multiplicative speckle noise which is inherently present in medical ultrasound images degrades the important clinical informations and badly affects the quality of the diagnosis. It is necessary to reduce the speckle noise to improve the visual quality of ultrasound images for better diagnoses. In this paper, a wavelet based method for despeckling of the ultrasound images is introduced where a local Wiener filter along with speckle reducing anisotropic diffusion (SRAD) filter are employed in a homomorphic framework. The signal variance in the local wiener filter is estimated from the output image of the SRAD filter. Since the size and shape of the locally adaptive window is an important issue in estimating the signal variance, nearly arbitrarily shaped windows are used for better performance. The experimental results using synthetically speckled ultrasound images show that the speckle noise is reduced to a great extent while preserving the important clinical information. In order to demonstrate the effectiveness of the proposed method, the method is compared with several other existing methods in terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM), edge preservation index ( ), and standard deviation to mean (S/M) ratio.

Polarimetric SAR statistical analysis using alpha-stable distribution and its application in optimal despeckling

In this article, the statistical model of the polarimetric synthetic aperture radar (SAR) single-look complex image is analysed using alpha-stable distribution. It is better to use alpha-stable distribution than Gaussian distribution to represent the statistical characteristics of the polarimetric SAR image. A polarimetric SAR covariance matrix estimation method based on fractional lower-order statistics (FLOS) is proposed. Based on this model, an adaptive polarimetric SAR optimal despeckling method based on FLOS is developed. This algorithm adaptively estimates the characteristic exponents of each channel and uses these estimated alphas to calculate the parameters for the optimal despeckling adaptively. The experiments using polarimetric SAR data demonstrate that the proposed method not only reduces the blurs that occur in the area of impulsive reflectors in the result of the original optimal despeckling method, but also maintains the speckle reduction ability (equivalent number of looks).

Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images

In this paper, the problem of despeckling SAR images when the input data is either an intensity or an amplitude signal is revisited. State-of-the-art despeckling methods based on Bayesian estimators in the wavelet domain, recently proposed in the literature, are taken into consideration. First, how these methods proposed for one format (e.g., intensity) can be adapted to the other format (e.g., amplitude) is investigated. Second, the performance of such algorithms in both cases is analyzed. Experimental results carried out on simulated speckled images and on true SAR data are presented and discussed in order to assess the best strategy. From these results, it can be observed that filtering in the amplitude domain yields better performances in terms of objective quality indexes, such as preservation of structural details, as well as in terms of visual inspection of the filtered SAR data.

SAR Image Despeckling with Adaptive Multiscale Products Based on Directionlet Transform

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise generated by radar coherent wave. In this paper, a new despeckling algorithm based on directionlets using multiscale products is proposed. We first take an anisotropic directionlet transform on the logarithmically transformed SAR images and multiply the coefficients at adjacent scales to enhance the details of image under consideration. Then, different from traditional thresholding methods, a threshold is applied to the multiscale products of the directionlet coefficients to suppress noise. Since the multiplication amplifies the significant features of signal and dilute noise, the proposed method reduces noise effectively while preserving edge structures. Finally, we compare the performance of the proposed algorithm with other despeckling methods applied to synthetic image and real SAR images. Experimental results demonstrate the effectiveness of the proposed method in SAR images despeckling.

Artifact-Free Despeckling of SAR Images Using Contourlet

Contourlets have been attracting increasing attention in despeckling of synthetic aperture radar (SAR) images in recent years. However, contourlets produce undesirable artifacts while despeckling. This letter presents a novel contourlet-based regularization method to remove speckle without introducing extra artifacts. In this method, a nonlocal regression function is constructed for the regularization term through patch weighting on the contourlet reconstruction. Moreover, by employing the intrinsic dependence of contourlet coefficients, a parameterized shrinkage algorithm is proposed to resolve the contourlet reconstruction. This contourlet despeckling method enables significant elimination of speckle and resultant artifacts with well-protected strong scatters and structures of the scene. Experiments demonstrate such superiority on real SAR images.

Generic computing of enhanced speckle reduction on low end ultrasound machine images

Due to the emergence of microelectronics, ultrasound diagnostic machine can now be manufactured at low cost. The affordability of low cost ultrasound diagnostic machine is of utter importance in allowing wider exposure of ultrasound technology to the general public. Nonetheless, in comparison with international standard requirements, the quality of images from low cost ultrasound machine is still lacking. Furthermore, the evaluation of the quality is subjective and the process is tedious. In this paper, an enhanced processing of ultrasound images using de-speckle diffusion method is proposed and the result is evaluated quantitatively on aspects such as vertical and horizontal distance accuracy, axial and lateral resolution, near field distance, penetration depth and high scatter accuracy. It comprises of the directions north, south, east and west, and the average is taken into calculation by utilizing the instantaneous coefficient of variation (ICOV) method. Algorithms are verified according to its performance in speckle reduction and edge preservation based on root mean square error (RMSE), signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR). The result indicates that de-speckle diffusion is more robust compared to adaptive speckle reduction method. The results obtained proved that the proposed technique could greatly improve the low cost ultrasound image quality. Key words: Speckle, reduction, ultrasound, images, diffusion, filtering, image processing.

Evaluation of Bayesian Despeckling and Texture Extraction Methods Based on Gauss–Markov and Auto-Binomial Gibbs Random Fields: Application to TerraSAR-X Data

Speckle hinders information in synthetic aperture radar (SAR) images and makes automatic information extraction very difficult. The Bayesian approach allows us to perform the despeckling of an image while preserving its texture and structures. This model-based approach relies on a prior model of the scene. This paper presents an evaluation of two despeckling and texture extraction model-based methods using the two levels of Bayesian inference. The first method uses a Gauss–Markov random field as prior, and the second is based on an auto-binomial model (ABM). Both methods calculate a maximum a posteriori and determine the best model using an evidence maximization algorithm. Our evaluation approach assesses the quality of the image by means of the despeckling and texture extraction qualities. The proposed objective measures are used to quantify the despeckling performances of these methods. The accuracy of modeling and characterization of texture were determined using both supervised and unsupervised classifications, and confusion matrices. Real and simulated SAR data were used during the validation procedure. The results show that both methods enhance the image during the despeckling process. The ABM is superior regarding texture extraction and despeckling for real SAR images.

An Adaptive Method of Speckle Reduction and Feature Enhancement for SAR Images Based on Curvelet Transform and Particle Swarm Optimization

This paper proposes an adaptive method based on the mirror-extended curvelet transform and the improved particle swarm optimization (PSO) algorithm, which reduce speckle noise and enhance edge features and contrast of synthetic aperture radar (SAR) images. First, an improved gain function, which integrates the speckle reduction with the feature enhancement, is introduced to nonlinearly shrink and stretch the curvelet coefficients. Then, a novel objective criterion for the quality of the despeckled and enhanced images is proposed in order to adaptively obtain the optimal parameters in the gain function. Finally, the PSO algorithm is employed as a global search strategy for the best despeckled and enhanced image. In order to increase the convergence speed and avoid the premature convergence, two further improvements for the classic PSO algorithm are presented. That is, a new learning scheme and a mutation operator are introduced. Experimental results demonstrate that the proposed method can efficiently reduce the speckle and enhance the edge features and the contrast of SAR images and outperforms the wavelet- and curvelet-based nonadaptive despeckling and enhancement methods.

Texture based Anisotropic Diffusion for Real Ultrasound Image Despeckling

This paper presents a new texture based anisotropic diffusion method for real ultrasound image despeckling. Texture information is obtained by a real ultrasound image model. Unlike traditional anisotropic diffusion methods usually taking image gradient as a diffusion index, we take the image texture as a new diffusion index. The results comparing our new method with others on both simulated image and real ultrasound images are reported, and our method shows the superiority in keeping important features of real ultrasound images.

Despeckling of medical ultrasound images using Daubechies complex wavelet transform

The paper presents a novel despeckling method, based on Daubechies complex wavelet transform, for medical ultrasound images. Daubechies complex wavelet transform is used due to its approximate shift invariance property and extra information in imaginary plane of complex wavelet domain when compared to real wavelet domain. A wavelet shrinkage factor has been derived to estimate the noise-free wavelet coefficients. The proposed method firstly detects strong edges using imaginary component of complex scaling coefficients and then applies shrinkage on magnitude of complex wavelet coefficients in the wavelet domain at non-edge points. The proposed shrinkage depends on the statistical parameters of complex wavelet coefficients of noisy image which makes it adaptive in nature. Effectiveness of the proposed method is compared on the basis of signal to mean square error (SMSE) and signal to noise ratio (SNR). The experimental results demonstrate that the proposed method outperforms other conventional despeckling methods as well as wavelet based log transformed and non-log transformed methods on test images. Application of the proposed method on real diagnostic ultrasound images has shown a clear improvement over other methods.