A novel method of Synthetic Aperture Radar (SAR) image despeckling using the contourlet transform representation is presented. Justification for the use of the contourlet signal representation, originally developed for natural images, is given. Methods of evaluating the despeckling performance of various algorithms are provided. Finally, a comparison of performance of multilook processing, wave...

Synthetic Aperture Radar (SAR) systems are all-weather, night and day, imaging systems. Automatic interpretation of information in SAR images is very difficult because SAR images are affected by a noise-like characteristic called speckle that arises from an imaging device and strongly data and makes automatic image interpretation very difficult. The speckle noise in SAR images can be removed us...

Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands, POSA is applied to the high subbands, and reco...

Contrast and quality of ultrasound images are adversely affected by the excessive presence of speckle. However, being an inherent imaging property, speckle helps in tissue characterization and tracking. Thus, despeckling of the ultrasound images requires the reduction of speckle extent without any oversmoothing. In this letter, we aim to address the despeckling problem using an unsupervised dee...

Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-dependent noise called speckle, which is very severe may hinder image exploitation. Despeckling an important task that aims to remove such so as improve the accuracy of all downstream processing tasks. The first despeckling methods date back 1970s, several model-based algorithms have been developed in years ...

Speckle noise, inherent in synthetic aperture radar (SAR) images, degrades the performance of the various SAR image analysis tasks. Thus, speckle noise reduction is a critical preprocessing step for smoothing homogeneous regions while preserving details. This letter proposes a variational despeckling approach where `1-norm total variation regularization term is approximated in a quadratic and l...

In this paper, we present a Stationary Wavelet Transform (SWT) based method for the purpose of despeckling the Synthetic Aperture radar (SAR) images by applying a maximum a posteriori probability (MAP) condition to estimate the noise free wavelet coefficients. The solution of the MAP estimator is based on the assumption that the wavelet coefficients have a known distribution. Rayleigh distribut...

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear endto-end mapping between the noisy and clean SAR images with a dilated residual network (SARDRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter s...

Synthetic Aperture Radar (SAR) imagery greatly suffers from multiplicative speckle noise, typical of coherent image acquisition sensors, such as SAR systems. Therefore, a proper and accurate despeckling preprocessing step is almost mandatory to aid the interpretation and processing of SAR data by human users and computer algorithms, respectively. Very recently, a scattering-oriented version of ...

Ultrasound Imaging plays vital role in diagnoses a disease. US image suffers from speckle noise. Despeckling is an important task for accurate diagnosis. In this paper experiment has been performed to measure the effectiveness of various filters available for despeckling. Results are compared qualitatively and quantitatively the Peak Signal to Noise Ratio and SSIM parameters are used to quantif...