Speckle noise reduction in images using Wiener filtering and adaptive Wavelet thresholding

Ultrasound (US) images are useful in medical diagnosis. US is preferred over other medical diagnosis technique because it is non-invasive in nature and has low cost. The presence of speckle noise in US images degrades its usefulness. A method that reduces the speckle noise in US images can help in correct diagnosis. This method also should preserve the important structural information in US images while removing the speckle noise. In this paper, a method for removing speckle noise using a combination of wavelet, total variation (TV) and morphological operations has been proposed. The proposed method achieves denoising by combining the advantages of the wavelet, TV and morphological operations along with the utilization of adaptive regularization parameter which controls the amount of smoothing during denoising. The work in this paper has the capability of reducing speckle noise while preserving the structural information in the denoised image. The proposed method demonstrates strong denoising for synthetic and real ultrasound images, which is also supported by the results of various quantitative measures and visual inspection.

An adaptive neuro-fuzzy inference system (ANFIS) based method is proposed for speckle noise reduction in synthetic aperture radar (SAR) images. Before using active RADAR (radio detection and ranging) and SAR imageries, the very first step is to reduce the effect of speckle noise. Reduction of speckle noise is one of the most important processes to increase the quality of radar coherent images. Filtering is the common method which is used to reduce the speckle noise. For this purpose, two ANFISs are trained and outputs of these systems are converted to one output through a mean calculator in this work. Performance of the proposed method is compared with performances of state-of-the-art methods in the literature for speckle noise reduction. Results are presented by filtered images and a table.

This paper undertakes the Automatic region extracting system by reduction of speckle noise reduction and regions extracting by segmentation in (Synthetic Aperture Radar) SAR images. Multiplicative speckle noise is inherently affected the SAR images Due to the coherent in nature. Noise density has been removed by using a Lee filter, Kuan filter, Weiner filter and NLM filter. The desired filtered SAR image result has a significant reduction in its variance with preserving the mean value of noise image. We apply the efficient methodology to a set of SAR speckle corrupted images. The results are evaluated on local quality and global criteria that shows the excellent proposal potential.

Speckle noise formed in Synthetic Aperture Radar (SAR) images makes visual and automatic analyses complicated. Thus, reducing speckle noise in homogeneous regions while preserving features such as edges and point scatterers is important as a pre-processing step. Although SAR images predominantly contains multiplicative noise, it also contains low amount of additive noise. In this study, a sparsity-driven multiplicative noise reduction method is proposed that also take additive noise component into account. Performance of the proposed methods is shown on SAR images.

Synthetic Aperture Radar (SAR) imagery is available all the time and, in all weathers, due to its ability to penetrate through cloud cover, dust, haze except heavy rainfall. SAR imagery mainly characterizes structural properties of various earth objects and provides a data which is rich in spatial information. Processing of SAR data has been of great interest due to these advantages. However, due to coherent processing of SAR signal, imagery is corrupted by speckle noise which is multiplicative in nature. Reduction of speckle noise from SAR imagery is helpful for analysis and interpretation of SAR images. In this paper, performance of conventional speckle reduction filters, namely Lee, Enhanced Lee, Frost, Enhanced Frost and Gamma MAP are compared with wavelet-based filtering technique for RISAT-1 imagery. Results are compared visually and using quality metrics, Speckle Suppression Index (SSI), Equivalent Number of Looks (ENL) and Speckle Suppression and Mean Preservation Index (SMPI). From visual interpretation of de-speckled image, it is evident that wavelet-based filtering out performs the conventional filters in terms of edge and texture preservation.

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.

Speckle Noise Reduction in Ultrasound Biomedical B-Scan Images Using Discrete Topological Derivative

Restoration of speckle noise corrupted SAR images using regularization by denoising

One of the major factors plaguing the performance of synthetic aperture radar (SAR) imagery is the presence of signal-dependent speckle noise. Grainy in appearance, speckle noise is primarily due to the phase fluctuations of the electromagnetic return signals. Since inherent spatial-correlation characteristics of speckle in SAR images are not exploited in existing multiplicative models for speckle noise, a new approach is proposed here that provides a new mathematical framework for modeling and reduction of speckle noise. The contribution of this paper is twofold. First, a novel model for speckled SAR imagery is introduced based on Markov random fields (MRFs) in conjunction with statistical optics. Second, utilizing the model, a global energy-minimization algorithm, based on simulated annealing (SA), is introduced for speckle reduction. In particular, the joint conditional probability density function (cpdf) of the intensity of any two points in the speckled image and the associated correlation function are used to derive the cpdf of any center pixel intensity given its four neighbors. The Hammersley- Clifford theorem is then used to derive the energy function associated with the MRF. The SA, built on the Metropolis sampler, is employed for speckle reduction. Four metrics are used to assess the quality of the speckle reduction: the mean-square error, SNR, an edge-preservation parameter and the equivalent number of looks. A comparative study using both simulated speckled images as well as real SAR images indicates that the proposed approach performs favorably in comparison to existing filtering techniques such as the modified-Lee and the enhanced Frost-algorithms.

Speckle noise is a kind of disturbance which appears as granular noise in Synthetic Aperture Radar (SAR) images. It makes the images quite difficult to interpret. Over the years, many filters have been used to mitigate this noise, with limited success. This work will primarily compare the Mean, Median, Guided Image Filter and a proposed filter for their effectiveness in reducing the speckle noise in SAR images. To measure it quantitatively, standard metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) are used. They help in assessing, and making a base to compare these filters. The comparison shows that the proposed filter and the Guided Image Filter, perform better in reducing noise and maintaining the structure of the image compared to older methods like Mean and Median Filters. This study may help in identification of new strategies for reducing speckle noise in SAR images, offering valuable insights for improving SAR image processing.

Images captured by synthetic aperture radar (SAR) are generally corrupted with noises. SAR images are commonly affected by speckle noise and impulse noise. Noise filtering techniques must remove noises and simultaneously preserve valuable information present in the images. This article presents a noise filtering techniques based on soft computing. The proposed filters are implemented in two phases: In the first phase, the presence of noise in a particular pixel is detected by using fuzzy logic, and in the second phase, noisy pixels are filtered by using Self-Organizing Map (SOM). From our experiment, it is found that the proposed SOM filter reduces both speckle and impulse noise and also preserves the information present in the image. The proposed SOM filter is comparatively evaluated with various filters based on peak signal-to-noise ratio and edge-preserving factor.

This article presents an overview of various denoising methods for optical and synthetic aperture radar (SAR) images. Currently, there are numerous algorithms and techniques to denoise images using adaptive filters in both the spatial as well as wavelet domain. However, in contrast, an algorithm which performs both soft and hard thresholding on a multi-level wavelet transformed image utilizing an adaptive threshold value has been designed and implemented. The threshold value varies for different wavelet regions from image to image and is selected for each region based on certain performance criteria such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). This algorithm has been used to denoise SAR images corrupted with multiplicative noise as well as optical images corrupted with White Gaussian additive noise. The results obtained have been compared with existing filters like median filter, Frost filter and Wiener filter. Additionally, we have compared the results of using different wavelet families including family of Daubechies and Biorthogonal filter banks.

In this Letter, we propose a fast speckle noise reduction method with only a single reconstructed image based on convolutional neural networks. The proposed network has multi-sized kernels that can capture the speckle noise component effectively from digital holographic images. For robust noise reduction performance, the network is trained with a large noisy image dataset that has object-dependent noise and a wide range of noise levels. The experimental results show the fast, robust, and outstanding speckle noise reduction performance of the proposed approach.

Synthetic aperture radar (SAR) image target recognition has important application values in security reconnaissance and disaster monitoring. However, due to speckle noise and target orientation sensitivity in SAR images, traditional spatial domain recognition methods face challenges in accuracy and robustness. To effectively address these challenges, we propose a dual-branch spatial-frequency domain fusion recognition method with cross-attention, achieving deep fusion of spatial and frequency domain features. In the spatial domain, we propose an enhanced multi-scale feature extraction module (EMFE), which adopts a multi-branch parallel structure to effectively enhance the network’s multi-scale feature representation capability. Combining frequency domain guided attention, the model focuses on key regional features in the spatial domain. In the frequency domain, we design a hybrid frequency domain transformation module (HFDT) that extracts real and imaginary features through Fourier transform to capture the global structure of the image. Meanwhile, we introduce a spatially guided frequency domain attention to enhance the discriminative capability of frequency domain features. Finally, we propose a cross-domain feature fusion (CDFF) module, which achieves bidirectional interaction and optimal fusion of spatial-frequency domain features through cross attention and adaptive feature fusion. Experimental results demonstrate that our method achieves significantly superior recognition accuracy compared to existing methods on the MSTAR dataset.

The paper proposes a method for ultrasound image denoising by using a classical signal processing method, i.e. Independent Component Analysis. The main idea is to process ultrasound images by the sparse code shrinkage algorithm based on ICA. We use the FastICA algorithm to estimate the inverse of the unknown mixing matrix and then apply the shrinkage operator for each determined independent component. The sparse code shrinkage method is compared with other speckle noise filtering algorithms and the results obtained show that sparse code shrinkage is a good method for multiplicative noise reduction in both test images and ultrasound images.