S. Kalaivani Narayanan

Bio: S. Kalaivani Narayanan is an academic researcher from Mount Zion College of Engineering and Technology. The author has contributed to research in topics: Speckle pattern & Real image. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

Despeckling of medical diagonostic ultrasound images via Laplacian based mixed PDE

Abstract: In this paper, we propose an efficient noise reduction method that can be used to reduce speckle and jointly enhancing the edge information, rather than just inhibiting smoothing. In this method speckle is removed by filtering of band pass ultrasound images in Laplacian pyramid domain by using mixed PDE based nonlinear diffusion. In each pyramid layer, a gradient threshold is estimated automatically using robust median estimator. The mean absolute error (MAE) between two adjacent diffusion steps is used as stopping criterion. Quantitative results on synthetic data and simulated phantom show the performance of the proposed method compared to state of the art methods. Results on real images demonstrate that the proposed method is able to preserve edges & structural details of the image.

Coupled PDE in pyramid domain for despeckling of B-scan images

Abstract: In this paper, we propose an efficient noise reduction method that can be used to reduce speckle and jointly enhancing the edge information, rather than just inhibiting smoothing. In this method speckle is removed by filtering of band pass ultrasound images in Laplacian pyramid domain by using coupled PDE based nonlinear diffusion. In each pyramid layer, a gradient threshold is estimated automatically using robust median estimator. The mean absolute error (MAE) between two adjacent diffusion steps is used as stopping criterion. Quantitative results on synthetic data and simulated phantom show the performance of the proposed method compared to state of the art methods. Results on real images demonstrate that the proposed method is able to preserve edges & structural details of the image.

Despeckling of SAR Images via an Improved Anisotropic Diffusion Algorithm

Abstract: Synthetic Aperture Radar (SAR) is a powerful tool for producing high-resolution images but these images are highly contaminated with speckle noise. This paper proposes an improved Anisotropic Diffusion Algorithm for despeckling SAR images. The proposed algorithm is obtained by using a diffusion coefficient which consists of a combination of first and second order derivative operators. The spatial variation of this diffusion coefficient occurs in such a way that it prefers forward diffusion to backward diffusion resulting in improved structural details and edge preservation. The simulation results also show better computational efficiency in comparison to other denoising techniques.