Explore the vast range of titles available.

Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.

Change detection in SAR is currently known to be a happening field of research in the domain of computer vision and remote sensing. There are numerous approaches and techniques available to detect the change in varied types of images captured in various areas. In this paper, we present a technique for detecting changes in SAR images, which generally happen to be poor contrast and poor brightness grayscale images. Consequently, they are complicated to change detection. We developed a change detection strategy in this study that employs a Convolution Neural Network (CNN) as a classification model. Further, we have used the Fuzzy Local Information C-Means approach to find out interesting pixels which have a high possibility of being changed or unchanged. By integrating the CNN classification result with the pre-classification result, the final change map is created. In this paper, we have tried to generate some virtual samples to supplement the lack of training samples. The efficiency and robustness of the planned approach have been ascertained with investigational results on four real SAR image data sets compared to several existing methodologies.

— Ultrasound imaging is the most commonly used imaging system in medical field. Main problem related to this imaging technique is introduction of speckle noise, thus making the image unclear. The success of ultrasonic examination depends on the image quality which is usually retarded due to speckle noise. There have been several techniques for effective suppression of speckle noise present in ultrasound images. The filtering techniques considered include anisotropic diffusion, wavelet de-noising, and local statistics. Comparison of the filters is based on their application of objective quality metrics, which quantifies the preservation of image edges, overall image distortion, and improvement in image contrast. The computational analysis quantifies the number of operations required for each speckle reduction method. A speed-accuracy analysis of various methods for anisotropic diffusion is included. It is concluded that the optimal method is the OSRAD (Oriented Speckle Reducing Anisotropic Diffusion) filter. The proposed approach technique deals with an improved OSRAD filter which gives an efficient result other than the previous filters by analysing the quality metrics.

Ultrasound (US) imaging is an indispensible technique for detection of abdominal stones which are a serious health hazard. Segmentation of stones from abdominal ultrasound images presents a unique challenge because these images contain strong speckle noise and attenuated artifacts. In clinical situations where a large number of stones must be identified, traditional methods such as manual identification become tedious and lack reproducibility too. The necessity of obtaining high reproducibility and the need to increase efficiency motivates the development of automated and fast procedures that segment out stones of all sizes and shapes in medical images by applying image segmentation techniques. In this paper we present and compare two fully automatic and unsupervised methods for robust stone detection in B-mode ultrasound images of the abdomen. Our approaches are based on the marker controlled watershed segmentation, along with some pre-processing and post-processing procedures that eliminate the inherent problems associated with medical ultrasound images. The first algorithm (Algorithm I) utilizes the advantage of the Speckle reducing anisotropic diffusion (SRAD) technique, along with unsharp filtering and histo- gram equalization for removal of speckle noise, and the second algorithm (Algorithm II) is based on the log decompression model which too serves as a tool for minimization of speckle. Experimental results obtained from processing a set of 50 ultrasound images ensure the robustness of both the proposed algorithms. Comparative results of both the algorithms based on efficiency and relative error in stone area have been provided.

A multi-scale fuzzy c-means method integrated with particle swarm optimization (MsFCM-PSO) is proposed for ultrasound image segmentation. First, speckle reducing anisotropic diffusion is used to suppress noise in an ultrasound image and construct a series of images at multiple scales. Then the particle swarm optimization is incorporated into the multi-scale fuzzy c-means (MsFCM) to search for the global optima of cluster centers and update the membership of each pixel in a coarse-to-fine fashion. Finally, the image is segmented by assigning each pixel to the cluster with the highest membership. The method was validated on both synthetic and in vivo ultrasound images. It outperformed the traditional fuzzy c-means methods including MsFCM by 39.6% and 13.6%, in terms of the Pratt's figure of merit and segmentation accuracy, respectively. These results demonstrate that the MsFCM-PSO can provide an accurate tool for ultrasound image segmentation.

Underwater images are usually suffering from the issues of quality degradation, such as low contrast due to blurring details, color deviations, non-uniform lighting, and noise. Since last few decades, many researches are undergoing for restoration and enhancement for degraded underwater images. In this paper, we proposed a novel algorithm using modified anisotropic diffusion filter with dynamic color balancing strategy. This proposed algorithm performs based on an employing effective noise reduction as well as edge preserving technique with dynamic color correction to make uniform lighting and minimize the speckle noise. Furthermore, reanalyze the contributions and limitations of existing underwater image restoration and enhancement methods. Finally, in this research provided the detailed objective evaluations and compared with the various underwater scenarios for above said challenges also made subjective studies, which shows that our proposed method will improve the quality of the image and significantly enhanced the image.

Existing methods to eliminate the laser speckle noise in quantitative phase imaging always suffer from the loss of detailed phase information and the resolution reduction in the reproduced image. To overcome these problems, this paper proposes a speckle noise suppression method based on empirical mode decomposition. Our proposed method requires only one image without additional equipment and avoids the complicated process of searching the optimal processing parameters. In this method, we use empirical mode decomposition to highlight the high frequency information of the interference image and use the Canny operator to perform edge detection, so the diffusion denoising process is guided by high-precision detection results to achieve better results. To validate the performance of our proposed method, the phase maps processed by our proposed method are compared with the phase maps processed by the improved anisotropic diffusion equation method with edge detection, the mean filter method and the median filter method. The experimental results show that the method proposed in this paper not only has a better denoising effect but also preserves more details and achieves higher phase reconstruction accuracy.

To effectively balance speckle smoothing and preservation of edges and radiation, a novel anisotropic diffusion filter was developed that uses a directional coherent coefficient. The proposed filter effectively improves the edge detection operator of a traditional anisotropic diffusion filter. The new edge detection operator calculates 16 direction coherence coefficients to avoid the interference of the edge direction. For the diffusion function, the proposed method directly uses the detected directional coherent edge as the diffusion coefficient, which simplifies the calculation of the diffusion function and avoids the adverse effects of inaccurate estimation of the diffusion function threshold for a traditional anisotropic diffusion filter. The influence of the number of iterations and time steps on the proposed filter was analyzed. A series of experiments was conducted with a simulated image and three real synthetic-aperture radar images from different sensors. The results confirmed that the proposed method not only significantly reduces speckle but also effectively preserves the edge and radiation information of images.

To better reduce image speckle noise while also maintaining edge information in synthetic aperture radar (SAR) images, we propose a novel anisotropic diffusion algorithm using weighted Euclidean distance (WEDAD). Presented here is a modified speckle reducing anisotropic diffusion (SRAD) method, which constructs a new edge detection operator using weighted Euclidean distances. The new edge detection operator can adaptively distinguish between homogenous and heterogeneous image regions, effectively generate anisotropic diffusion coefficients for each image pixel, and filter each pixel at different scales. Additionally, the effects of two different weighting methods (Gaussian weighting and non-linear weighting) of de-noising were analyzed. The effect of different adjustment coefficient settings on speckle suppression was also explored. A series of experiments were conducted using an added noise image, GF-3 SAR image, and YG-29 SAR image. The experimental results demonstrate that the proposed method can not only significantly suppress speckle, thus improving the visual effects, but also better preserve the edge information of images.

This paper presents a novel algorithm for image denoising using an improved nonlinear diffusion PDE model and a cellular neural network (CeNN) scheme. In particular, the images corrupted with multiplicative (speckle) noise have been considered. The proposed generalized-order nonlinear diffusion (GOND) model is solved through a suitable cellular neural network (CeNN) approach. The CeNN templates act like edge-preserving filters to reduce the multiplicative noise. The present study also gives a convergence analysis of the proposed CeNN based solution scheme. Further, the proposed scheme is numerically validated on synthetic, medical, and real SAR images. The obtained results demonstrate that the proposed algorithm provides a better way to deal with speckle noise and suppresses the staircase effects. To broaden the simulation results, the proposed method is applied to images corrupted with Gaussian, Rayleigh, and gamma noise. The proposed method for SSIM values interpret 0.2dB to 0.4dB better than state-of-the-art methods and comparative results in terms of PSNR in most test cases.