Multiplicative Speckle Noise Research Articles

A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.

Polarimetric SAR image despeckling using bandelet transform based on additive–multiplicative speckle model

Efficient speckle filtering algorithms are required for the effective use of polarimetric synthetic aperture radar (SAR) technology in remote sensing and surveillance applications. Nevertheless many techniques have been proposed over the past two decades to decrease the speckle noise in polarimetric SAR images, they are all based on the multiplicative speckle noise model. In order to fully utilise the advantages of polarimetry of these images, an additive–multiplicative noise model is explored. Coupled with this, bandelet based Bayesian thresholding is used to tap the advantages of transform domain filtering. Here the elements of the covariance matrix are processed differently for diagonal and off-diagonal elements to achieve maximum benefits. The proposed filtering scheme is evaluated using airborne and spaceborne polarimetric images and compared against state-of-the-art techniques. Results indicate that the proposed method reduces the speckle content while retaining the geometrical features in these images.

Three-dimensional curvelet-based dictionary learning for speckle noise removal of optical coherence tomography.

Optical coherence tomography (OCT) is a recently emerging non-invasive diagnostic tool useful in several medical applications such as ophthalmology, cardiology, gastroenterology and dermatology. One of the major problems with OCT pertains to its low contrast due to the presence of multiplicative speckle noise, which limits the signal-to-noise ratio (SNR) and obscures low-intensity and small features. In this paper, we recommend a new method using the 3D curvelet based K-times singular value decomposition (K-SVD) algorithm for speckle noise reduction and contrast enhancement of the intra-retinal layers of 3D Spectral-Domain OCT (3D-SDOCT) images. In order to benefit from the near-optimum properties of curvelet transform (such as good directional selectivity) on top of dictionary learning, we propose a new plan in dictionary learning by using the curvelet atoms as the initial dictionary. For this reason, the curvelet transform of the noisy image is taken and then the noisy coefficients matrix in each scale, rotation and spatial coordinates is passed through the K-SVD denoising algorithm with predefined 3D initial dictionary that is adaptively selected from thresholded coefficients in the same subband of the image. During the denoising of curvelet coefficients, we can also modify them for the purpose of contrast enhancement of intra-retinal layers. We demonstrate the ability of our proposed algorithm in the speckle noise reduction of 17 publicly available 3D OCT data sets, each of which contains 100 B-scans of size 512×1000 with and without neovascular age-related macular degeneration (AMD) images acquired using SDOCT, Bioptigen imaging systems. Experimental results show that an improvement from 1.27 to 7.81 in contrast to noise ratio (CNR), and from 38.09 to 1983.07 in equivalent number of looks (ENL) is achieved, which would outperform existing state-of-the-art OCT despeckling methods.

Retinal OCT Denoising with Pseudo-Multimodal Fusion Network.

Optical coherence tomography (OCT) is a prevalent imaging technique for retina. However, it is affected by multiplicative speckle noise that can degrade the visibility of essential anatomical structures, including blood vessels and tissue layers. Although averaging repeated B-scan frames can significantly improve the signal-to-noise-ratio (SNR), this requires longer acquisition time, which can introduce motion artifacts and cause discomfort to patients. In this study, we propose a learning-based method that exploits information from the single-frame noisy B-scan and a pseudo-modality that is created with the aid of the self-fusion method. The pseudo-modality provides good SNR for layers that are barely perceptible in the noisy B-scan but can over-smooth fine features such as small vessels. By using a fusion network, desired features from each modality can be combined, and the weight of their contribution is adjustable. Evaluated by intensity-based and structural metrics, the result shows that our method can effectively suppress the speckle noise and enhance the contrast between retina layers while the overall structure and small blood vessels are preserved. Compared to the single modality network, our method improves the structural similarity with low noise B-scan from 0.559 ± 0.033 to 0.576 ± 0.031.

Performance Analysis of Various Filters for De-Speckling of Thyroid Ultrasound Images

Thyroid ultrasonography is the most common and extremely useful, safe, and cost effective way to image the thyroid gland and its pathology. However, an inherent characteristic of Ultrasound (US) imaging is the presence of multiplicative speckle noise. Speckle noise reduces the ability of an observer to distinguish fine details, make diagnosis more difficult. It limits the effective implementation of image analysis steps such as edge detection, segmentation and classification. The main objective of this study is to compare the performance of various spatial and frequency domain filters so as to identify efficient and optimum filter for de-speckling Thyroid US images. The performance of these filters is evaluated using the image quality assessment parameters Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Square Error (MSE) and Root Mean Square Error (RMSE) for different speckle variance. Experimental work revealed that kuan filter resulted in higher PSNR, SNR, SSIM and least MSE, RMSE values compared to other filters

Multi-Layer Abstraction Saliency for Airport Detection in SAR Images

The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods easily result in inaccurate detection due to the complex scenes and multiplicative speckle noise. Therefore, airport detection from SAR images is still a challenging task. In order to limit the influence of unnecessary and attractive details and noise, we propose a multi-layer abstraction saliency model for airport detection in SAR images in this paper. Specifically, we first obtain airport support regions and superpixels in the first layer. According to the dis-similarity between foreground and background superpixels, airport components are explored by iterative refinement for each airport support region in the second layer. In the third layer, airport adobes are produced by clustering. Based on the characteristics of an airport in SAR images, we propose three saliency cues, including local contrast (LC), adobe deformation (AD), and global uniqueness (GU), to obtain adobe-level saliency. Furthermore, we assign saliency to each pixel by Bayesian inference. Finally, we can explore airport location using integrated saliency map. The proposed approach is tested on an airport data set collected from Gaofen-3, TerraSAR, and RadarSat. Our method achieves 88.89% detection rate. The experimental results demonstrate that the proposed algorithm is effective and outperforms the previously airport detection methods. The code will be available at https://github.com/NengyuanLiu/MyAirportSaliency .

Spatially adaptive polarimetric image despeckling using bandelet transform

ABSTRACT Polarimetric Synthetic Aperture Radar (PolSAR) imaging extended SAR applications by exploring the polarimetric properties of the target scatterers. Similar to SAR images, PolSAR images are prone to multiplicative speckle noise due to its coherent imaging mechanism. Hence, despeckling is an essential procedure for effectively utilizing the PolSAR images for remote sensing applications. Latest advances in PolSAR filtering techniques have shown the trend of using spatial domain techniques based on multiplicative speckle noise model which impairs the retrieved image by oversmoothing and absence of image finer details. More efficient despeckling algorithm using bandelet thresholding is proposed in this paper. Since the bandelet transform is computationally expensive, a spatially adaptive technique using directional variance is proposed. Performance evaluation with both airborne and spaceborne radar images has shown the approached technique is much efficient related to speckle reduction and feature preservation compared to the state of art techniques.

EXTRACTION OF FETAL FEATURES FROM B MODE ULTRASONOGRAMS FOR EFFICIENT DIAGNOSIS OF DOWN SYNDROME IN FIRST AND SECOND TRIMESTER

Segmentation of ultrasound images has been found to be a tedious task due to the presence of speckle and other artifacts. The random nature of the multiplicative speckle noise and lack of demarcation of information in ultrasound images makes the segmentation a highly complex one. In this paper a modified watershed based method has been proposed for segmentation of features from Ultrasound images towards efficient diagnosis of Down Syndrome in first and second trimester. The pixels are grouped based on the pixel differences and the co- occurrence matrix is formed based on the energy and contrast. If global scheme is adopted for segmentation the high frequency edges may appear as artifacts. Hence to overcome this the wavelet transform of co-occurrence matrix is obtained and each decomposed band is subjected to an averaging filter. The process bands are thresholded using Otsu’s method and a binary image is obtained. The isolated pixels are removed by using suitable morphological operations. Then inverse wavelet transform is performed to obtain the image skeleton. The resultant image is subjected to watershed segmentation using gradients. Using the above mentioned approach we can see that the regions of interest are clearly segmented and is producing reproducible results.

SAR Speckle Nonlocal Filtering With Statistical Modeling of Haar Wavelet Coefficients and Stochastic Distances

Due to the coherent processing of synthetic aperture radar (SAR) systems, multiplicative speckle noise arises providing a granular appearance in SAR images. This kind of noise makes it difficult to analyze and interpret surface images from the earth. Therefore, studying alternatives to attenuate the speckle is a constant task in the image processing literature. Current state-of-the-art filters in remote sensing area explore the philosophy of similarity between patches. This paper aims to expand the traditional nonlocal means (NLM) algorithm originally proposed for the additive white Gaussian noise (AWGN) to deal with the speckle. In our research, we consider the worst scenario, i.e., the single-look speckle noise, and apply the NLM to filter intensity SAR images in the Haar wavelet domain. To accomplish this task, the Haar coefficients were described by exponential-polynomial (EP) and gamma distributions. Furthermore, stochastic distances based on these two mentioned distributions were derived and embedded in the NLM filter by replacing the Euclidean distance of the original method. This represents the main contribution of the proposed research. Finally, this paper analyzes and compares the synthetic and real experiments of the proposed method with some recent filters of the literature demonstrating its competitive performance.

Speckle denoising in optical coherence tomography images using residual deep convolutional neural network

Optical Coherence Tomography (OCT) is an emerging imaging modality used for diagnosis of ocular diseases like age-related macular degeneration (AMD) and macular edema. OCT imaging is a non-invasive technique to capture cross-sectional volumes of the retinal areas of human eye. Due to coherent nature of image acquisition process, OCT images suffer from granular multiplicative speckle noise. Presence of speckle noise in OCT images makes its clinical analysis difficult for the experts. The same is the problem with the development of computer aided diagnosis (CAD) systems for detection of ocular diseases. Speckle noise is granular in nature and interferes with the diagnostic observations made using OCT images and the segmentation of different OCT layers. This work presents an efficient OCT denoising technique using residual convolutional neural network. The proposed technique will not only help experts in analysis of OCT images, but can also act as first step to construct CAD systems for ocular diseases. The performance of the proposed approach is evaluated on Duke (SD-OCT) and Topcon (3D-OCT) image databases based on visual and parametric observations. The performance of the proposed method on parameters like PSNR, SSIM, MSR, CNR, and ENL is compared with the state-of-the-art speckle denoising methods. It is observed that the proposed approach performs better as compared to the methods referred from literature on both visual and parametric evaluations.

Sparse Representation Based Despeckling of SAR Images using STDTCWT

Synthetic Aperture Radar (SAR) is a coherent acquisition system and hence images obtained from Synthetic Aperture Radar are difficult to analyze due to the multiplicative speckle noise present in the images. Therefore, despeckling of SAR images is a primary speckle reduction task in SAR image processing. All the existing Despeckling methods focus on speckle reduction but not all methods can preserve the image edge features in SAR images. This paper propose a despeckling algorithm via sparse representation using hybrid Shearlet Transform and Dual Tree Complex wavelet transform that posses both shift invariant and direction selectivity properties in images. The experimental results show that the proposed hybrid Shearlet and Dual Tree Complex Wavelet Transform outperforms the existing methods of denoising in terms of PSNR, ENL, and EPI for MSTAR dataset images. The proposed methodology not only preserves the edges but also enhances the image visually.

SAR Image Registration Using an Improved SAR-SIFT Algorithm and Delaunay-Triangulation-Based Local Matching

Synthetic aperture radar (SAR) image registration is a challenging task in remote sensing because of the presence of significant intensity as well as geometric differences between the images. Moreover, the influence of multiplicative speckle noise in SAR images adversely affects the registration result. The synthetic aperture radar-scale invariant feature transform (SAR-SIFT) algorithm, specially developed for the SAR images, has been used popularly for the automatic registration of SAR images based on global feature matching. However, the algorithm suffers from lack of controllability of the number of extracted features and uneven distribution of the features. Moreover, global matching of the features reduces the number of correct matches. To address these problems, we propose an improved SAR-SIFT (I-SAR-SIFT) algorithm, which extracts features from the SAR images by considering three important factors of feature-based image registration: stability, distinctiveness, and distribution. Moreover, a novel approach is adapted to improve the distinctiveness of the feature–descriptor in the presence of speckle noise. In addition, a Delaunay-triangulation-based local matching algorithm is introduced to significantly improve the matching performance in SAR images. Experiments on different pairs of SAR images show that the proposed method gives better performance than the existing SAR image registration methods.

Anisotropic Diffusion Based Multiplicative Speckle Noise Removal.

Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. Firstly, an anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation of the image, is proposed. Although the proposed model can effectively remove multiplicative speckle noise, it does not consider the noise at the edge during the denoising process. Hence, we decompose the divergence term in order to make the diffusion at the edge occur along the boundaries rather than perpendicular to the boundaries, and improve the model to meet our requirements. Secondly, the iteration stopping criteria based on kurtosis and correlation in view of the lack of ground truth in real image experiments, is proposed. The optimal values of the parameters in the model are obtained by learning. To improve the denoising effect, post-processing is performed. Finally, the simulation results show that the proposed model can effectively remove the speckle noise and retain minute details of the images for the real ultrasound and RGB color images.

Optimal Canny’s Parameters Regressions for Coastal Line Detection in Satellite-Based SAR Images

Canny’s algorithm is a very well-known and widely implemented multistage edge detector. The extraction of coastal lines in space-borne-based synthetic aperture radar (SAR) images using this algorithm is particularly complicated because of the multiplicative speckle noise present in them and can only be used if Canny’s parameters (CaPP) are chosen appropriately. This letter introduces a methodology for computing functional forms for the CaPP, using functions of the image characteristics through a system that combines artificial neural networks (ANN) with statistical regression. A set of CaPP functional forms is obtained by applying this method on synthetic SAR images. Pratt’s figure of merit (PFoM) is used to measure the performance of them, obtaining more than 0.75, on average, in the 14400 synthetic SAR images analyzed. Finally, this set of formulas has been tested for extracting coastal edges from real polynyas SAR images, acquired from Sentinel-1.

OCT image speckle sparse noise reduction based on dictionary algorithm

As a new non-invasive and high-resolution scanning method, optical coherence tomography (OCT) has been widely used in clinical practice, but OCT images have serious speckle noise, which greatly affects the diagnosis of diseases. Two original dictionary noise reduction algorithms have been improved for multiplicative speckle noise in OCT. The algorithm first performs logarithmic transformation on OCT images, uses orthogonal matching pursuit algorithm for sparse coding, and K singular value decomposition learning algorithm to update adaptive dictionary. Finally, it returns to the space domain through weighted average and exponential transformation. The experimental results show that the improved two dictionary algorithms can effectively reduce the speckle noise in OCT images and obtain good visual effects. The noise reduction effect is evaluated by four factors: mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and edge-preserving index (EPI). Compared with the two original dictionary noise reduction algorithms and the traditional filtering algorithms, the noise reduction effect of the two improved dictionary algorithms is better than that of other algorithms, and the improved adaptive dictionary algorithm performs better.

Resolutional Analysis of Multiplicative High-Frequency Speckle Noise Based on SAR Spatial De-Speckling Filter Implementation and Selection

Due to the inherent characteristics of the electromagnetic wave scattering phenomenon, synthetic aperture radar (SAR) images are directly degraded by high-frequency multiplicative speckle (HMS) noise, which makes image de-speckling filter application and selection a challenge. In this regard, an adverse effects analysis of the HMS under implementation of seven different spatial de-speckling filters on a reference SAR image is considered in this paper. The investigation includes the formulation of the backscattered data and the HMS based on the pixel statistics and their distribution as an image noise behavioral analysis method. The resulting complex behavioral model is used for HMS power spectral density (PSD) function modeling. This paper also includes HMS system resolution effects analysis on the raw data generation (RDG) and the received frequency profile (RFP). An objective quality assessment procedure was also carried out to investigate both the de-speckled image resolution and the spatial filter evaluation in the presence of the HMS. As a result, the simulations verify that speckles are embedded within the high-frequency parts of the raw data, directly affecting the spatial resolution and the image resolution with non-specific patterns. The results also show that no spatial de-speckling filter consistently outperforms others, and their implementation is completely dependent on the texture, the system parameters, and their evaluation index. As a novel approach, HMS spectral behavioral modeling within the filtered images, as well as the proposed spatial de-speckling filter evaluation methods, are the proper techniques for optimum filter selection and specific purpose applications. The results are very helpful for remote sensing image restoration and data preservation when dealing with SAR images with a less fine resolution, such as ice-covered areas, coastal change detection, vegetation texture detection, geological structures mapping, and so forth. The SAR system resolution analysis is completed based on inversed problem solution (IPS) and with the help of a hybrid-domain image formation algorithm (IFA).

Discontinuity adaptive SAR image despeckling using curvelet-based BM3D technique

Synthetic Aperture Radar (SAR) is an all-weather, day and night satellite imaging technology where the radar is mounted on aircraft and successive pulses of radio waves are transmitted to illuminate the target scene. The signal processing of the recorded backscattered echoes produce SAR images. SAR images contain inherent multiplicative speckle noise which is formed due to the constructive and destructive interference of transmitted signals with the returning signals. Speckle noise appears as granular patterns and makes the image interpretation difficult. Non-local means approaches like Block Matching and 3D filtering (BM3D) are effective scheme for removing speckle noise from SAR images. This method gives good performance for additive noise but is not adaptive to curved edges and discontinuities that occur in SAR images affected by multiplicative noise. This paper proposes a three-step refined algorithm to adapt BM3D for despeckling multiplicative speckle noise. In the proposed scheme curvelet is used to find the transform coefficients and this modification in the transform domain improves the despeckling accuracy of BM3D. Also Wiener filtering is replaced with Importance Sampling Unscented Kalman Filtering (ISUKF) for better adapting to discontinuities in the real SAR image. An improved method of grouping is proposed here based on Manhattan distance which better adapts to constantly changing multiplicative noise statistics. A detailed comparative study is carried out on each step using various well-known performance measures. From the results, it is found that the proposed Curvelet-ISUKF-Manhattan BM3D (CIM-BM3D) method of despeckling has better values for all the performance measure and the results are also visually verified.

Despeckling of Carotid Artery Ultrasound Images with a Calculus Approach.

Carotid artery images indicate any presence of plaque content, which may lead to atherosclerosis and stroke. Early identification of the disease is possible by taking B-mode ultrasound images in the carotid artery. Speckle is the inherent noise content in the ultrasound images, which essentially needs to be minimized. The objective of the proposed method is to convert the multiplicative speckle noise into additive, after which the frequency transformations can be applied. The method uses simple differentiation and integral calculus and is named variable gradient summation. It differs from the conventional homomorphic filter, by preserving the edge features to a great extent and better denoising. The additive image is subjected to wavelet decomposition and further speckle filtering with three different filters Non Local Means (NLM), Vectorial Total Variation (VTV) and Block Matching and 3D filtering (BM3D) algorithms. By this approach, the components dependent on the image are identified and the unwanted noise content existing in the high frequency portion of the image is removed. Experiments conducted on a set of 300 B-mode ultrasound carotid artery images and the simulation results prove that the proposed method of denoising gives enhanced results as compared to the conventional process in terms of the performance evaluation methods like peak signal to noise ratio, mean square error, mean absolute error, root mean square error, structural similarity, quality factor, correlation and image enhancement factor.

A Gabor Odd Filter-Based Ratio Operator for SAR Image Matching

Synthetic aperture radar (SAR) image matching is a challenging issue in remote sensing as the images contain significant multiplicative speckle noise. Histogram of oriented gradients (HOG)-based descriptors have been used popularly for the matching of the SAR images. An appropriate gradient computation of the SAR images plays a significant role in the matching performance of these HOG-based descriptors. The ratio operators are preferred for computing the gradients in the SAR images as these are usually corrupted by the multiplicative noise. Recently, a multiscale Gabor odd filter-based ratio operator (GOFRO) has been proposed for the edge detection of the SAR images. In this letter, we utilize the GOFRO for SAR image matching. We propose a Gabor odd filter (GOF)-based descriptor, where the gradients are computed by the GOFRO. At first, scale-invariant feature transform (SIFT) features are extracted from the SAR images. A novel approach is presented to select the more repeatable SIFT features in SAR images. Then, the proposed GOF-based descriptor is formed for the SIFT features and, finally, feature matching is performed. Experiments on three sets of simulated and real SAR image pairs demonstrate the effectiveness of the proposed GOF-based descriptor for SAR image matching.

Compound denoising method of laser speckle noise in laser inherent field imaging

Noise is an important factor affecting the image quality of laser coherent field high resolution imaging system. And there exists not only background light additive noise but also laser multiplicative speckle noise in a laser coherent field imaging system. Both of the above noise affect the imaging quality of laser coherent field system. In order to improve the imaging quality from the perspective of noise suppression and settle the imaging quality degradation problem of laser multiplicative speckle noise and background additive noise in the laser coherent field imaging system, the model for the influence of multiplicative speckle noise and background additive noise on laser echo field demodulated signal is established in atmospheric downlink. Then, based on the model, a novel homomorphic filter and sparse matrix trace cascade compound de-noising algorithm is put forward. Firstly, based on the homomorphic filtering theory, the laser multiplicative speckle noise in the laser echo demodulated signal is converted into the additive noise by logarithmic transformation. Then the low-frequency laser multiplicative speckle noise is filtered by the high-pass filter, and the high-frequency demodulated signal is retained. The logarithmic inverse transform is used to obtain the laser echo demodulation signal after the multiplicative speckle noise has been filtered out. Next, the phase random disturbance of atmosphere in laser echo demodulated signal is suppressed by phase closure technology and the imaging spectrum component is reconstructed by the spectrum iterative reconstruction method. Then the high resolution image is obtained by spectrum component inverse Fourier transform. Finally, the effect of background additive noise on the image quality is suppressed by the sparse base tracking theory. The simulated and outdoor experiment result are used to verify the denoising effect and image quality enhancement effect of the composite de-noising method. Compared with the existing single denoising method, the composite denoising method is shown to be able to effectively eliminate laser multiplicative speckle noise and background additive noise at one time. The proposed method can improve image contrast and promote the Strehl ratio of imaging quality in a coherent imaging system. It provides a theoretical basis for improving imaging quality and denosing laser multiplicative speckle noise and background additive noise in coherent field imaging system.