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Ultrasound is a diagnostic imaging technique to detect various illnesses related to the body's internal organs. It is a non-invasive, safe, cost-effective, and simple imaging modality that uses high-frequency sound waves and their reflection from internal organs. However, a key drawback of using ultrasound imaging tools is that they are often contaminated by multiplicative speckle noise, which can reduce the image resolution and contrast and complicates the diagnosis of the tiny structure of lesions. Extensive studies have shown that eliminating speckle noise is a necessary pre-processing task for computer-assisted ultrasound image systems. This paper proposes a novel method for denoising, weighted thresholding, and fusion of ultrasound images in the Non-Subsampled Contourlet Transformation (NSCT) domain. The method utilizes (a) The Bayesian and Bivariate shrinkage rule in the NSCT domain, (b) A new local structural similarity-fusion-based homomorphic ultrasound image despeckling method in the NSCT domain, and (c) A weights estimation approach based on a bilateral method that helps improve noise suppression and edge preservation results. The performance of the proposed algorithm is evaluated in terms of mean squared error (MSE), peak signal-to-noise ratio, structural similarity index (SSIM), speckle suppression index, signal-to-MSE ratio, and speckle signal-to-noise ratio. Extensive computer simulation results show that the presented method performs better in subjective visual inspection than existing state-of-the-art despeckling techniques.

This paper presents a new technique for despeckling of Synthetic Aperture Radar (SAR) images using a local correlation based fusion of high-frequency coefficients in Discrete Wavelet Transform (DWT) with method noise thresholding. The decomposition level is decided by analyzing the texture of the input image at each level by calculating entropy. The core idea of the proposed technique lies in the selection of decomposition level in 2D-DWT based on entropy parameter and on the fusion of high-frequency coefficients. On decomposition, the low-frequency coefficients remain untouched and the high-frequency coefficients are thresholded using two different shrinkage rules. Therefore the Bayesian and Bivariate shrinkage methods are applied to the high-frequency coefficients. After performing two different thresholding methods, the improved high-frequency coefficients are fused using local correlation based strategy. The threshold value is calculated by correlation strategy. Later the correlation coefficient (CC) is evaluated between the two improved high-frequency coefficients. The CC is now compared with the threshold value for the fusion purpose. On the basis of defined fusion strategy, the average and maximum operation are applied to perform the fusion of high-frequency coefficients. The despeckling scheme is followed by method noise thresholding in order to preserve the fine details of the image. The performance of the proposed method is assessed using metrics such as Signal-to-Noise Ratio (SNR), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM) and visual appearance of the despeckled image. The experimental results demonstrate the effectiveness of proposed work over prior works on SAR image despeckling.

In synthetic aperture radar (SAR) images, degradation due to multiplicative speckle noise and detail blurring is one of the common problem. This problem is solved by despeckling method. The main objective of image despeckling is to eliminate the speckle noise and preserve the important details of SAR images such as texture, edges, structures and corners. Usually SAR images are high dimensional images and preserving the edge and corner components is one major issue. Anisotropic diffusion also called Perona-Malik diffusion is used to reduce noise without disturbing the significant parts of the image. An homomorphic scheme is proposed using anisotropic diffusion in db2-type wavelet transform. Linear and non-linear filters are used on the approximate part of the image to remove blurring. Method noise thresholding is used to restore the unfiltered part of the despeckled image. The proposed method is applied and tested on correlated speckle noise as well as uncorrelated speckle noise on the real dataset of SAR images. The performance of the proposed method is evaluated by its visual quality and by using other metrics such as PSNR, SSIM, UIQI and RMSE. The performance and computational time are calculated and compared with standard filters and methods. The critical analysis of the result shows that proposed method gives the brilliant outcome in terms of structure, edge preservation and noise suppression. The proposed method has the ability to be used in practical applications. Keywords: Image despeckling, Wavelet transform, Anisotropic diffusion, Method noise

The removal of granular pattern multiplicative speckle noise is the major issue in Synthetic Aperture Radar (SAR) images. Theoretically, multiplicative noise is considered as the ratio of the standard deviation to the signal value. The proposed scheme works on db2 based 2D-Discrete Wavelet Transform (DWT) using wavelet thresholding and method noise. The scheme is designed to despeckle the simulated SAR images and real SAR images. It uses a hybrid combination of Directional Smoothing Filter (DSF), wavelet thresholding using enhanced Bayesian shrinkage rule and method noise for despeckling. After DWT decomposition, the approximate component is directed to DSF followed by method noise thresholding and detailed components are subjected to wavelet thresholding followed by method noise thresholding. The article justifies the efficient use of method noise and explains how its exact use can enhance the result of the algorithm over other efficient methods. The quality assessment of the proposed method is done by visual appearance and measures such as Peak Signal-to-Noise Ratio (PSNR), Structural similarity index metric (SSIM), Universal Image Quality Index (UIQI), Equivalent Numbers of Looks (ENL), Noise Variance (NV), Coefficient of Variation (CV), Mean Squared Error (MSE), Correlation Coefficient (CC) and preservation of mean values before and after despeckling. The effectiveness of the proposed method is demonstrated by matching it to well-known speckle noise removal methods on SAR images. The proposed method has the capability to be used in real time practical applications. Keywords: SAR image, Speckle noise, DWT, Directional smoothing filter, Method noise