Saima Yasmeen¹, Muhammad Usman Yaseen^1,*, Syed Sohaib Ali², Moustafa M. Nasralla³, Sohaib Bin Altaf Khattak³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3671-3689, 2023, DOI:10.32604/cmc.2023.041195

Abstract SAR images commonly suffer from speckle noise, posing a significant challenge in their analysis and interpretation. Existing convolutional neural network (CNN) based despeckling methods have shown great performance in removing speckle noise. However, these CNN-based methods have a few limitations. They do not decouple complex background information in a multi-resolution manner. Moreover, they have deep network structures that may result in many parameters, limiting their applicability to mobile devices. Furthermore, extracting key speckle information in the presence of complex background is also a major problem with SAR. The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based… More >

T. Manikandan¹, S. Karthikeyan^2,*, J. Jai Jaganath Babu³, G. Babu⁴

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2755-2771, 2023, DOI:10.32604/iasc.2023.030497

Abstract Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer, resulting in an inaccurate diagnosis. As a result, it needs an efficient despeckling method for ultrasound images in clinical practice and telemedicine. This article proposes a novel adaptive fuzzy filter based on the directionality and translation invariant property of the Non-Sub sampled Contour-let Transform (NSCT). Since speckle-noise causes fuzziness in ultrasound images, fuzzy logic may be a straightforward technique to derive the output from the noisy images. This filtering method comprises detection and filtering stages. First, image regions classify at the detection stage by applying… More >

P. L. Joseph Raj, K. Kalimuthu^*, Sabitha Gauni, C. T. Manimegalai

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1187-1196, 2022, DOI:10.32604/iasc.2022.026052

Abstract Speckle Reduction Anisotropic Diffusion filter which is used to despeckle ultrasound images, perform well at homogeneous region than in heterogeneous region resulting in loss of information available at the edges. Extended SRAD filter does the same, preserving better the edges in addition, compared to the existing SRAD filter. The proposed Extended SRAD filter includes the intensity of four more neighboring pixels in addition with other four that is meant for SRAD filter operation. So, a total of eight pixels are involved in determining the intensity of a single pixel. This improves despeckling performance by maintaining the information accessible at an… More >

Memoona Malik^*, Iftikhar Azim, Amir Hanif Dar, Sohail Asghar

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 165-182, 2021, DOI:10.32604/iasc.2021.017437

Abstract Despeckling of SAR imagery is a crucial step prior to their automated interpretation as information extraction from noisy images is a challenging task. Though a huge despeckling literature exists in this regard, there is still a room for improvement in existing techniques. The contemporary despeckling techniques adversely affect image edges during the noise reduction process and are thus responsible for losing the significant image features. Therefore, to preserve important features during the speckle reduction process, a two phase hybrid despeckling filter is proposed in this study. The first phase of the hybrid filter focuses on edge preservation by employing a… More >

Ranu Gupta^1,3,*, Rahul Pachauri^2,3, Ashutosh Singh^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.1, pp. 19-32, 2018, DOI:10.3970/cmes.2018.114.019

Abstract This article presents an improved method of despeckling the ultrasound medical images. In this paper a modified local statistics mean variance filter method has been proposed. In the proposed method, more consideration is given to local statistics since local statistical features are more important rather than global features.Various parameters like mean square error, peak signal to noise ratio, quality index, and structural similarity index measure are calculated to analyze the quality of the despeckled image. More >