Speckle Noise In Images Research Articles

SAR Ship Image Speckle Noise Suppression Algorithm Based on Adaptive Bilateral Filter

Aiming at the shortcomings of traditional bilateral filtering in suppressing speckle noise in SAR ship images, especially strong speckle noise and loss of image edge details, it proposes an improved bilateral filtering algorithm based on fast adaptive threshold and variable window in this paper. The algorithm was used to suppress speckle noise in SAR ship images. The traditional bilateral filtering cannot effectively filter out the strong speckle noise, but the SAR image has strong speckle noise because of the defects of its imaging principle. To solve these problems, an image sample truncation method based on fast adaptive truncation statistical characteristics is designed to adjust the photometric similarity weight characteristics to realize the adaptive adjustment of spatial standard deviation and gray standard deviation. After the local reference window is modified and truncated according to the local characteristics of the image, the adjusted combined similarity weight value greatly reduces the impact of strong speckle noise on the image. It is smoothed into speckle signal with strong impulse noise. In the traditional bilateral filtering, in order to enhance the effect of smoothing noise, it is necessary to specify a large value of geometric diffusion factor and gray similarity diffusion factor, resulting in the loss of image details. Based on the variable window size filtering method, when the extended local reference window is in the case of nonuniform edge, its window can be enlarged to make the speckle noise stronger. When an extended window contains details such as edges and textures, its size is not expanded to maintain image detail. This method can further smooth the speckle noise in the uniform region while preserving the edge details of the image. Finally, the adaptive truncated sample is used as the input of the bilateral filter. The image sample truncation method based on fast adaptive threshold can effectively eliminate the strong speckle noise information that affects the photometric similarity and weight accuracy of the image. The method based on variable window can greatly enhance the smoothness of the edge area of the image. The experimental results show that improved adaptive bilateral filtering algorithm improves the speckle noise removal ability by 16.06% compared with the traditional bilateral filtering algorithm in the speckle noise suppression of the SAR ship image, and the preservation performance of the image edge after filtering is improved by 5.41%. Compared with the original image, the filtered image has a 1.2% improvement in structural similarity. The algorithm can effectively suppress speckle noise and has a good ability to retain edge and texture information of the SAR ship image, which has strong practicability.

Offshore Bridge Detection in Polarimetric SAR Images Based on Water Network Construction Using Markov Tree

It is difficult to detect bridges in synthetic aperture radar (SAR) images due to the inherent speckle noise of SAR images, the interference generated by strong coastal scatterers, and the diversity of bridge and coastal terrain morphologies. In this paper, we present a two-step bridge detection method for polarimetric SAR imagery, in which the probability graph model of a Markov tree is used to build the water network, and bridges are detected by traversing the graph of the water network to determine all adjacent water branch pairs. In the step of the water network construction, candidate water branches are first extracted by using a region-based level set segmentation method. The water network is then built globally as a tree by connecting the extracted water branches based on the probabilistic graph model of a Markov tree, in which a node denotes a single branch and an edge denotes the connection of two adjacent branches. In the step of the bridge detection, all adjacent water branch pairs related to bridges are searched by traversing the constructed tree. Each bridge is finally detected by merging the two contours of the corresponding branch pair. Three polarimetric SAR data acquired by RADARSAT-2 covering Singapore and Lingshui, China, and by TerraSAR-X covering Singapore, are used for testing. The experimental results show that the detection rate, the false alarm rate, and the intersection over union (IoU) between the recognized bridge body and the ground truth are all improved by using the proposed method, compared to the method that constructs a water network based on water branches merging by contour distance.

SAR ship target detection method based on CNN structure with wavelet and attention mechanism.

Ship target detection in synthetic aperture radar (SAR) images is an important application field. Due to the existence of sea clutter, especially the SAR imaging in huge wave area, SAR images contain a lot of complex noise, which brings great challenges to the effective detection of ship targets in SAR images. Although the deep semantic segmentation network has been widely used in the detection of ship targets in recent years, the global information of the image cannot be fully utilized. To solve this problem, a new convolutional neural network (CNN) method based on wavelet and attention mechanism was proposed in this paper, called the WA-CNN algorithm. The new method uses the U-Net structure to construct the network, which not only effectively reduces the depth of the network structure, but also significantly improves the complexity of the network. The basic network of WA-CNN algorithm consists of encoder and decoder. Dual tree complex wavelet transform (DTCWT) is introduced into the pooling layer of the encoder to smooth the speckle noise in SAR images, which is beneficial to preserve the contour structure and detail information of the target in the feature image. The attention mechanism theory is added into the decoder to obtain the global information of the ship target. Two public SAR image datasets were used to verify the proposed method, and good experimental results were obtained. This shows that the method proposed in this article is effective and feasible.

DHNet: High-resolution and hierarchical network for cross-domain OCT speckle noise reduction.

Optical coherence tomography (OCT) imaging uses the principle of Michelson interferometry to obtain high-resolution images by coherent superposing of multiple forward and backward scattered light waves with random phases. This process inevitably produces speckle noise that severely compromises visual quality of OCT images and degrades performances of subsequent image analysis tasks. In addition, datasets obtained by different OCT scanners have distribution shifts, making a speckle noise suppression model difficult to be generalized across multiple datasets. In order to solve the above issues, we propose a novel end-to-end denoising framework for OCT images collected by differentscanners. The proposed model utilizes the high-resolution network (HRNet) as backbone for image restoration, which reconstructs high-fidelity images by maintaining high-resolution representations throughout the entire learning process. To compensate distribution shifts among datasets collected by different scanners, we develop a hierarchical adversarial learning strategy for domain adaption. The proposed model is trained using datasets with clean ground truth produced by two commercial OCT scanners, and then applied to suppress speckle noise in OCT images collected by our recently developed OCT scanner, BV-1000 (China Bigvision Corporation). We name the proposed model as DHNet (Double-H-Net, High-resolution and Hierarchical Network). We compare DHNet with state-of-the-art methods and experiment results show that DHNet improves signal-to-noise ratio by a large margin of 18.137 dB as compared to the best of our previous method. In addition, DHNet achieves a testing time of 25 ms, which satisfies the real-time processing requirement for the BV-1000 scanner. We also conduct retinal layer segmentation experiment on OCT images before and after denoising and show that DHNet can also improvesegmentation. The proposed DHNet can compensate domain shifts between different data sets while significantly improve speckle noise suppression. The HRNet backbone is utilized to carry low- and high-resolution information to recover fidelity images. Domain adaptation is achieved by a hierarchical module through adversarial learning. In addition, DHNet achieved a testing time of 25 ms, which satisfied the real-time processingrequirement.

Fast and accurate sea-land segmentation of SAR imagery based on multi-source data registration

Targeted on accurate and efficient ship detection of Synthetic Aperture Radar imagery, sea-land segmentation is key for the complex port scenes. Despite the importance of this task, current methods are still severely influenced by several limitations, such as high level of image speckle noise, complicated backscattering characteristics. In order to overcome these issues, we propose a sea-land segmentation method of Synthetic Aperture Radar imagery based on multi-source data registration. To be specific, high-resolution optical remote sensing images and high-precision coastline vector data are integrated with Synthetic Aperture Radar images for accurate sea-land segmentation. First, the Pyramid Level-by-level Guided-Histogram of the Orientated Phase Congruency method is developed for the registration of Synthetic Aperture Radar and optical images, making two types of images into the same spatial reference. Following this, the high-precision coastline vector data collected from optical images is transferred to the Synthetic Aperture Radar image, realizing accurate sea-land segmentation. Finally, aiming at ship detection tasks, two quantitative evaluation indicators for sea-land segmentation are proposed: (1) the Absolute Segmentation Accuracy; and (2) the Ship Mis-segmentation Index. The accuracy and efficiency of the proposed method are tested and verified on Gaofen-3 SAR images of Yokosuka Port, Japan. Experimental results show that the proposed method achieves an Absolute Segmentation Accuracy of 3.15 pixels, a Ship Mis-segmentation Index of 9.23% and processing time of 82.92 s on average, which reveals that the proposed method outperforms current popular approaches in terms of accuracy and efficiency.

WUnet: A new network used for ultrasonic tongue contour extraction

Ultrasound imaging is becoming a practical tool in silent speech recognition. It is a challenge to accurately extract tongue contours due to the soft tissue characteristics of the tongue and the high level of speckle noise in ultrasound images. Based on the U-Net network, an improved network called wUnet is proposed to extract the contour of the ultrasonic tongue. First, upward interlevel jump connections are added to the coding network to extract sufficient tongue contour features at different levels to learn more coarse-grained information. Second, downward interlevel jump connections are added to the decoding network to have richer fine-grained semantics and more obvious image reconstruction effects in the information fusion stage. Finally, VGG16 convolutional blocks with a different number of layers are added between the two interlevel connections, and an end-to-end multilevel context encoder is jointly trained. The binary-cross entropy, IoU, and Dice coefficients are fused to form a composite function to calculate the network loss, which accelerates the process of determining the contour boundary. Extensive experiments on the NS, TJU, and TIMIT datasets show that the proposed approach yields a better extraction result on ultrasonic tongue contour than the baseline models.

Artificial intelligence based detection of age-related macular degeneration using optical coherence tomography with unique image preprocessing.

The aim of the study is to improve the accuracy of age related macular degeneration (AMD) disease in its earlier phases with proposed Capsule Network (CapsNet) architecture trained on speckle noise reduced spectral domain optical coherence tomography (SD-OCT) images based on an optimized Bayesian non-local mean (OBNLM) filter augmentation techniques. A total of 726 local SD-OCT images were collected and labelled as 159 drusen, 145 dry AMD, 156 wet AMD and 266 normal. Region of interest (ROI) was identified. Speckle noise in SD-OCT images were reduced based on OBNLM filter. The processed images were fed to proposed CapsNet architecture to clasify SD-OCT images. Accuracy rates were calculated in both public and local dataset. Accuracy rate of local SD-OCT image dataset classification was achieved to a value of 96.39% after performing data augmentation and speckle noise reduction with OBNLM. The performance of proposed CapsNet was also evaluated on the public Kaggle dataset under the same processing procedures and the accuracy rate was calculated as 98.07%. The sensitivity and specificity rates were 96.72% and 99.98%, respectively. The classification success of proposed CapsNet may be improved with robust pre-processing steps like; determination of ROI and denoised SD-OCT images based on OBNLM. These impactful image preprocessing steps yielded higher accuracy rates for determining different types of AMD including its precursor lesion on the both local and public dataset with proposed CapsNet architecture.

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning.

The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases and so on. However, the presence of excessive speckle noise in breast ultrasound images can greatly reduce the image resolution and affect the observation and judgment of patients’ condition. Therefore, it is particularly important to investigate image speckle noise suppression. In the paper, we propose fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning. Firstly, according to the gray value of the breast ultrasound image, the input breast ultrasound image contrast is enhanced using logarithmic and exponential transforms, and guided filter algorithm was used to enhance the details of glandular ultrasound image, and spatial high-pass filtering algorithm was used to suppress the excessive sharpening of breast ultrasound image to complete the pre-processing of breast ultrasound image and improve the image clarity; Secondly, the pre-processed breast ultrasound images were input into the 3D convolutional cloud neural network image speckle noise suppression model; Finally, the edge sensitive terms were introduced into the 3D convolutional cloud neural network to suppress the speckle noise of breast ultrasound images while retaining image edge information. The experiments demonstrate that the mean square error and false recognition rate all reduced to below 1.2% at the 100th iteration of training, and the 3D convolutional cloud neural network is well trained, and the signal-to-noise ratio of ultrasound image speckle noise suppression is greater than 60 dB, the peak signal-to-noise ratio is greater than 65 dB, the edge preservation index value exceeds the experimental threshold of 0.45, the speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible. The speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible, which can be applied to the field of breast ultrasound diagnosis.

Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network.

Computed tomography (CT) is widely used in medical diagnoses due to its ability to non-invasively detect the internal structures of the human body. However, CT scans with normal radiation doses can cause irreversible damage to patients. The radiation exposure is reduced with low-dose CT (LDCT), although considerable speckle noise and streak artifacts in CT images and even structural deformation may result, significantly undermining its diagnostic capability. This paper proposes a multistage network framework which gradually divides the entire process into 2-staged sub-networks to complete the task of image reconstruction. Specifically, a dilated residual convolutional neural network (DRCNN) was used to denoise the LDCT image. Then, the learned context information was combined with the channel attention subnet, which retains local information, to preserve the structural details and features of the image and textural information. To obtain recognizable characteristic details, we introduced a novel self-calibration module (SCM) between the 2 stages to reweight the local features, which realizes the complementation of information at different stages while refining feature information. In addition, we also designed an autoencoder neural network, using a self-supervised learning scheme to train a perceptual loss neural network specifically for CT images. We evaluated the diagnostic quality of the results and performed ablation experiments on the loss function and network structure modules to verify each module's effectiveness in the network. Our proposed network architecture obtained high peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual information fidelity (VIF) values in terms of quantitative evaluation. In the analysis of qualitative results, our network structure maintained a better balance between eliminating image noise and preserving image details. Experimental results showed that our proposed network structure obtained better metrics and visual evaluation. This study proposed a new LDCT image reconstruction method by combining autoencoder perceptual loss networks with multistage convolutional neural networks (MSCNN). Experimental results showed that the newly proposed method has performance than other methods.

Применение метода перенормировки с ограничением к акустическим изображениям

The paper proposes the use of the method of renormalization with limitation (MRL) for suppressing the speckle noise in acoustic images. The method is tested on various types of acoustic images. The principal possibility of a significant reduction in the speckle noise level is found due to the fact that the MRL renormalizes the spectrum of the sonar image to the universal reference spectrum (URS) model, which is a model of the spectrum of a «good» quality optical image. To increase the overall sharpness, after applying MRL, it is recommended to use post-processing suitable for each specific type of image. It is proposed to estimate the degree of suppression of speckle noise using the standard deviation from the image averaged over the sprite. The study allows us to conclude that the application of MRL to various acoustic images can significantly reduce the speckle noise

Deep learning speckle de-noising algorithms for coherent metrology: a review and a phase-shifted iterative scheme [Invited

We present a review of deep learning algorithms dedicated to the processing of speckle noise in coherent imaging. We focus on methods that specifically process de-noising of input images. Four main classes of applications are described in this review: optical coherence tomography, synthetic aperture radar imaging, digital holography amplitude imaging, and fringe pattern analysis. We then present deep learning approaches recently developed in our group that rely on the retraining of residual convolutional neural network structures to process decorrelation phase noise. The paper ends with the presentation of a new approach that uses an iterative scheme controlled by an input SNR estimator associated with a phase-shifting procedure.

Enhancement algorithm for separability of inland water body in synthetic aperture radar image via sparse representation and image fusion

ABSTRACT In synthetic aperture radar (SAR) images, inland water bodies generally appear as dark textures, but due to the interference of thermal noise, speckle noise, and azimuth blur, inland water bodies can be easily confused with weakly scattered areas such as roads, wetlands, and sand. Most of the existing denoising methods have been proposed for removing speckle noise. However, the presence of thermal noise and azimuth blur still makes it difficult to distinguish inland water bodies. The denoising method based on the Doppler spectrum can effectively remove thermal noise, speckle noise, and azimuth blur, but it reduces the resolution, which blurs the contour of the water body, and it is difficult to apply to the detection and processing of small inland water bodies. Fusing a noisy high-resolution image with a low-resolution image with less noise makes it possible to remove the speckle noise and thermal noise of the SAR image while maintaining the edge texture of the water body. Based on the idea of image fusion, sparse representation is used to fuse the original noisy SAR image (HR) and the low-precision image (LR) processed by the denoising algorithm to maximise the use of all the information in HR and LR. According to this idea, an enhancement algorithm for separability of inland water body based on sparse representation is proposed. The experimental results show that the proposed model significantly improves the separability of inland water bodies in SAR images.

Enriching SAR Ship Detection via Multistage Domain Alignment

The advent of deep learning has made a significant advance in ship detection in synthetic aperture radar (SAR) images. However, it is still challenging since the amount of labeled SAR samples for training is not sufficient. Moreover, SAR images are corrupted by speckle noise, making them complex and difficult to interpret even by human experts. In this letter, we propose a novel SAR ship detection framework that leverages label-rich electro-optical (EO) images for more plentiful feature representations, and delicately addresses the speckle noise in SAR images. To this end, we first introduce a multistage domain alignment module that reduces the distribution discrepancies between EO and SAR feature maps at local, global, and instance levels. This allows enriching SAR representations by gradually instilling cross-domain knowledge from a large-scale EO image dataset. We further design a blind-spot layer for feature extraction to suppress the influence of speckles. Experimental results on the high-resolution SAR images dataset (HRSID) show that our detection performance achieves average precision (AP) 5.5% better than the current state-of-the-arts that exploits SAR images only. Our method significantly improves the detection performance with higher speckle noises, demonstrating stronger robustness than the conventional methods.

Mapping the 2021 October Flood Event in the Subsiding Taiyuan Basin by Multitemporal SAR Data

A flood event induced by heavy rainfall hit the Taiyuan basin in north China in early October of 2021. In this study, we map the flood event process using the multi-temporal synthetic aperture radar (SAR) images acquired by Sentinel-1. First, we develop a spatiotemporal filter based on low-rank tensor approximation (STF-LRTA) for removing the speckle noise in SAR images. Next, we employ the classic log-ratio change indicator and the minimum error threshold algorithm to characterize the flood using the filtered images. Finally, we relate the flood inundation to the land subsidence in the Taiyuan basin by jointly analyzing the multi-temporal SAR change detection results and interferometric SAR (InSAR) time-series measurements (pre-flood). The validation experiments compare the proposed filter with the Refined-Lee filter, Gamma filter, and an SHPS-based multi-temporal SAR filter. The results demonstrate the effectiveness and advantage of the proposed STF-LRTA method in SAR despeckling and detail preservation, and the applicability to change scenes. The joint analyses reveal that land subsidence might be an important contributor to the flood event, and the flood recession process linearly correlates with time and subsidence magnitude.

Underwater Sonar Image Segmentation by a Novel Joint Level Set Model

This paper proposes a novel joint level set model for underwater sonar image segmentation. Combining features in points and regions in our novel joint level set (LS), it can achieve excellent performance for underwater sonar image segmentation. Regional information guides the model to locate the object of interest, whereas the point information accurately delineates contours. In addition, the unified Markov random field (UMRF) is taken to measure the neighboring relation between points and regions, which can overcome the problems of the high speckle noise, strong bias and low resolution of underwater sonar images. Our novel model can segment underwater sonar images into three partitions, such as the objects of interest, shadow and backgrounds. In contrast to current segmentation methods, outstanding results are demonstrated by our model. Moreover, another advantage of our model lies in its high efficiency.

Complex Variational Inference Network for PolSAR Classification

For maintaining the phase information in images, complex neural networks have been widely applied to PolSAR classification. However, due to constant weights of neurons, the networks may lack randomness and be potentially overfitting for complicated imaging mechanisms and random speckle noise in PolSAR images. Thus, this letter proposes a complex variational inference network (CVIN) where complex Gaussian probability distributions are introduced into the weights of neurons in complex neural networks. In CVIN, a novel evidence lower bound (ELBO) for complex network is designed to infer the variational approximation of weights through backpropagation. After training, CVIN propagates the approximate posterior distributions given the data and makes the prediction of the labels. Thus, CVIN is an ensemble of flexible models with infinite weights, where the complex weights are regularized by the Gaussian distributions. Experiments on real PolSAR images verify the feasibility of CVIN and illustrate the potential of CVIN to serve as a competitive method for PolSAR classification.

An Adaptive PolSAR Trilateral Filter Based on the Mechanism of Scattering Consistency

Polarimetric Synthetic Aperture Radar (PolSAR) can record the backscattering information of ground objects by capturing the phase difference of echo signal in different polarization combinations. Due to the characteristics of coherent imaging, there is a lot of coherent speckle noise in PolSAR images. However, most of the current PolSAR image despeckling algorithms are difficult to balance the ability in aspects of speckle suppression, polarization information retention and edge structure preservation. This paper proposes an adaptive PolSAR trilateral filter (PTF) based on the mechanism of scattering consistency, which consists of following three steps. (1) The marker map of ground object scattering category is firstly obtained based on the dominant scattering mechanism determined by Freeman decomposition of the polarization covariance matrix. (2) Next, the neighborhood pixels with the same scattering mechanism as the center pixel are screened out within an adaptive filtering window. (3) A PolSAR trilateral filter is established through integration of the spatial proximity, polarization similarity and local structural characteristics of the center pixel and the screened pixels to effectively suppress the speckle noises. Experimental results upon the data of Radarsat-2 and AIRSAR demonstrate that PTF can not only suppress coherent speckle noise effectively without sacrificing the edge structure information, but also retain the unique polarization information of PolSAR image. In particular, compared with the traditional six widely used PolSAR despeckling algorithms, PTF have been significantly improved in terms of Equivalent Number of Looks (ENL) and Degree of difference (Dod) indexes by 2.49 times and 6 orders of magnitude, respectively.

SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration

Accurate registration between SAR images is the basis for high-precision geometric correction of SAR images. The feature points extracted by conventional feature extraction methods is unsatisfactory, which is affected by imaging geometric characteristics and speckle noise of SAR images. This paper innovatively proposes a spaceborne SAR image feature learning framework to realize automatic sample generation and model training. It mainly includes two modules: The feature sample generation module based on the initial geometric information of spaceborne SAR. The initial RPC parameters of the spaceborne SAR are adopted to realize the initial positioning of SAR image, and a variety of feature extraction operators are used to match the overlapping areas to obtain high-precision matching points, which are employed as training samples for image-pairs; Pseudo-Siamese feature learning network SARPointNet for SAR image feature learning. The pseudo-siamese network is used to extract the feature points and descriptors of the sample image-pairs. The feature optimization process is realized through the descriptor constraints between image pairs, which promotes the network to improve the accuracy of feature extraction. The proposed method has been tested in mountain, hilly, flatland and urban scenarios, respectively. The results demonstrate that the correspondence points extracted by SARPointNet are evenly distributed, large quantities (at least 10 times that of other methods) and achieve high precision (The root mean square error is less than 1 pixels), which shows great advantages over other methods.

FINet: A Feature Interaction Network for SAR Ship Object-Level and Pixel-Level Detection

Deep learning-based detection methods have achieved great success in ship target detection in synthetic aperture radar (SAR) images. However, due to the interference of imaging mechanism, speckle noise, and sea and land clutter, ship detection in SAR images still suffers from difficult interpretation. It is found that most ship detection algorithms focus on object-level detection while ignoring pixel-level information. In order to further improve the recognition effectiveness and positioning accuracy of ships in SAR images, we present a novel ship detection method based on a feature interaction network (FINet) in SAR images from the perspective of object-level and pixel-level. FINet consists of an object-level detection network and a pixel-level detection network. The information of the two branches is fused through the feature interaction module (FIM), and then the object-level information and pixel-level information are enhanced by the feature guidance module (FGM). Finally, FINet utilizes object-level and pixel-level detection heads for prediction and regression to obtain object-level classification accuracy, positioning bounding box coordinates, and pixel-level binary classification results. The experimental results demonstrate that the classification effectiveness and localization accuracy of FINet are better than those of the comparison algorithms, and FINet achieves the best performance.

Framelet transform and fuzzy clustering-based intelligent technique for speckle noise removal in ultrasound images

Purpose Speckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches. Design/methodology/approach The work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information. Findings The proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators. Originality/value Fuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.