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.

Synthetic aperture radar (SAR) images are difficult to analyze due to speckle noise, which is a characteristic of multiplicative noise. Over the last few decades, a number of studies have been performed regarding the removal of speckle noise. However, the existing studies exhibit edge information loss when removing speckle noise. In this paper, we propose an algorithm using speckle reducing anisotropic diffusion (SRAD), soft thresholding, and a guided filter to effectively remove speckle noise from SAR images while preserving edge information. The proposed algorithm first obtains a filtered image by applying an SRAD filter to a noise image. To further remove the multiplicative noise remaining in the filtered image, a logarithmic transformation is applied to convert it into additive noise. The filtering image was decomposed into multiresolution images using discrete wavelet transform (DWT). Soft thresholding and a guided filter were used for each of the high-frequency subimages and the low-frequency subimage. Then, an inverse DWT and an exponential transform are applied to the denoised image. The experimental results indicate that the proposed algorithm shows better performance than the conventional filtering method in terms of both objective and subjective performances.

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.

Ultrasound imaging devices are more secure and real-time than other medical diagnosis devices such as X-ray, CT, and MRI. However, due to speckle noise, it exhibits lower resolution and image quality than other than medical diagnostic devices. Therefore, it is difficult to diagnose lesions using ultrasound images due to this noise. In this paper, we propose an algorithm using speckle reducing anisotropic diffusion (SRAD) and a guided filter for speckle noise reduction and edge preservation. First, speckle, which is multiplicative noise, is removed by using SRAD. The remaining noise in the filtered image is converted to additive noise using logarithmic transformation. The additional noise remaining in the filtered image is further removed using a guided filter. Finally, the noise-free image obtained using exponential transform. Experimental results show that the proposed algorithm exhibits an excellent speckle noise reduction and edge preservation performance than the conventional filtering techniques.

Ultrasound imaging has been used for diagnosing lesions in the human body. In the process of acquiring ultrasound images, speckle noise may occur, affecting image quality and auto-lesion classification. Despite the efforts to resolve this, conventional algorithms exhibit poor speckle noise removal and edge preservation performance. Accordingly, in this study, a novel algorithm is proposed based on speckle reducing anisotropic diffusion (SRAD) and a Bayes threshold in the wavelet domain. In this algorithm, SRAD is employed as a preprocessing filter, and the Bayes threshold is used to remove the residual noise in the resulting image. Compared to the conventional filtering techniques, experimental results showed that the proposed algorithm exhibited superior performance in terms of peak signal-to-noise ratio (average = 28.61 dB) and structural similarity (average = 0.778).