Speckle Suppression Method Research Articles

Speckle Suppression in SAR Images Using the 2-D GARCH Model

A novel Bayesian-based speckle suppression method for Synthetic Aperture Radar ( SAR) images is presented that preserves the structural features and textural information of the scene. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the wavelet coefficients of SAR images have significantly non-Gaussian statistics that are best described by the 2-D GARCH model. By using the 2-D GARCH model on the wavelet coefficients, we are capable of taking into account important characteristics of wavelet coefficients, such as heavy tailed marginal distribution and the dependencies between the coefficients. Furthermore, we use a maximum a posteriori (MAP) estimator for estimating the clean image wavelet coefficients. Finally, we compare our proposed method with various speckle suppression methods applied on synthetic and actual SAR images and we verify the performance improvement in utilizing the new strategy.

Speckle reduction based on contourlet transform using scale adaptive threshold for medical ultrasound image

A new speckle suppression method in contourlet domain was presented. By modeling the subband contourlet coefficients of the ultrasound images after logarithmic transform as generalized Gaussian distribution (GGD), we gave a scale-adaptive threshold in Bayesian framework. Experimental results of both synthetic and clinical ultrasound images show that our method has a better performance on speckle suppressing than the wavelet-based method while well preserving the feature details.

Inter-scale wavelet analysis for speckle reduction in thyroid ultrasound images

A wavelet-based method for speckle suppression in ultrasound images of the thyroid gland is introduced. The classification of image pixels as speckle or part of important image structures is accomplished within the framework of back-propagation tracking and singularity detection of wavelet transform modulus maxima, derived from inter-scale analysis. A comparative study with other de-speckling techniques, employing quantitative indices, demonstrated that our method achieved superior speckle reduction performance and edge preservation properties. Moreover, a questionnaire regarding qualitative imaging parameters, emanating from various visual observations, was employed by two experienced physicians in order to evaluate the algorithm's speckle suppression efficiency.

Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images

This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, and the iterative edge enhancement feature of nonlinear diffusion. With fully exploited knowledge of speckle image models, the edges of images are detected using normalized wavelet modulus. Relying on this feature, both the envelope-detected speckle image and the log-compressed ultrasonic image can be directly processed by the algorithm without need for additional preprocessing. Speckle is suppressed by employing the iterative multiscale diffusion on the wavelet coefficients. With a tuning diffusion threshold strategy, the proposed method can improve the image quality for both visualization and auto-segmentation applications. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of noise suppression and edge preservation indices.

A diffusion stick method for speckle suppression in ultrasonic images

This paper presents a diffusion stick method for speckle suppression in ultrasonic images. An asymmetric stick filter kernel is firstly defined by decomposing the rectangle neighborhood into line segments of variable orientations. Then, the weighted sum of averages along each stick is used to produce the filtered images. Implemented in an iteration scheme, our method works as a pseudo-diffusion process, where the diffusivity is controlled by a normalized variance function. Experiments of synthetic and real images show that the diffusion stick technique performs effectively in suppressing speckle noise, preserving resolvable structures and enhancing linear features.

An adaptive speckle suppression and edge enhancement technique

Speckle degrades severely the quality of medical B-scan ultrasonic images, especially it blurs edges and details of images. An adaptive speckle suppression and edge enhancement method based on Nakagami distribution is presented. The statistics of log-compressed echo images is derived for Nakagami distribution. An adaptive filter based on local statistical property of speckle is designed. The stick technique that utilizes sticks with different sizes and various orientations is applied to locally approximate certain linear features of image. The local region is a stick instead of a usual window, the orientation of sticks is decided by hypothesis test optimizing method and the length of sticks is obtained by region growing technique. Performance of the new method has been tested on the phantom and ultrasound images of pig muscle and echocardiographic. The results show that the technique effectively reduces the speckle noise while preserving and enhancing the tissue edge and resolvable details.

Novel Bayesian multiscale method for speckle removal in medical ultrasound images

A novel speckle suppression method for medical ultrasound images is presented. First, the logarithmic transform of the original image is analyzed into the multiscale wavelet domain. We show that the subband decompositions of ultrasound images have significantly non-Gaussian statistics that are best described by families of heavy-tailed distributions such as the alpha-stable. Then, we design a Bayesian estimator that exploits these statistics. We use the alpha-stable model to develop a blind noise-removal processor that performs a nonlinear operation on the data. Finally, we compare our technique with current state-of-the-art soft and hard thresholding methods applied on actual ultrasound medical images and we quantify the achieved performance improvement.

Array detection for speckle reduction in optical coherence microscopy

This paper introduces a spatial-diversity method for speckle suppression in optical coherence microscopy. The method is based on combining interference signals from an array of detectors placed in the back focal plane of the objective lens, such that elements receive light backscattered from the sample volume at different angles. Incoherently adding (`compounding') the signals increases the signal-to-noise ratio of the processed image compared to that attainable with a single detector. The speckle-reduction method was demonstrated with a benchtop microscope equipped with a quadrant photodiode. To evaluate its potential application in dermatology, images of living skin acquired with and without compounding were compared. The quality of the compounded images was found to be substantially better. A signal-to-noise gain close to a factor of two (the theoretical maximum attainable using four detectors) was achieved without a significant loss in resolution. The method can be applied to arrays with a larger number of elements, potentially enabling more advanced forms of spatial-diversity and adaptive-optics methods.

An adaptive speckle suppression filter for medical ultrasonic imaging

An adaptive smoothing technique for speckle suppression in medical B-scan ultrasonic imaging is presented. The technique is based on filtering with appropriately shaped and sized local kernels. For each image pixel, a filtering kernel, which fits to the local homogeneous region containing the processed pixel, is obtained through a local statistics based region growing technique. The performance of the proposed filter has been tested on the phantom and tissue images. The results show that the filter effectively reduces the speckle while preserving the resolvable details. The simulation results are presented in a comparative way with two existing speckle suppression methods.