Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Gamma distribution

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    A fourth-order Partial Differential Equation model for multiplicative noise removal in images
    (IEEE Computer Society, 2013) Bini, A.A.; Bhat, M.S.
    In coherent imaging, the sensed images are usually corrupted with multiplicative data dependent noise. Unlike additive noise, the presence of multiplicative noise destroys the information content in the original image to a great extent. In this paper, we propose a new fourth-order Partial Differential Equation (PDE) model with a noise adaptive fidelity term for multiplicative Gamma noise removal under the variational Regularization framework. Variational approaches for multiplicative noise removal generally consist of a maximum a posteriori (MAP) based fidelity term and a Total-Variation (TV) regularization term. However, the second-order TV diffusion approximates the observed images with piecewise constant images, leading to the so-called block effect. The proposed model removes the multiplicative noise effectively and approximates observed images with planar ones making the restored images more natural compared to the second-order diffusion models. The proposed method is compared with the recent state-of-the art methods and the effective restoration capability of the filter is demonstrated experimentally. © 2013 IEEE.
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    Fourth order PDE based ultrasound despeckling using ENI classification
    (Institute of Electrical and Electronics Engineers Inc., 2016) Soorajkumar, R.; Krishna Kumar, P.; Girish, D.; Rajan, J.
    Medical ultrasound images are generally corrupted with a type of signal dependent noise called speckle. The major reason for the speckle in ultrasound images is the constructive or destructive interference of ultrasound waves. The granular pattern of the speckle noise degrades the image and hinders the information present in it. In this work, we developed an improved speckle denoising method using a fourth order partial differential equation (PDE) model by integrating Edge Noise Interior method in it. Edge Noise Interior (ENI) method preserves the edges and counts the number of homogeneous pixels in the neighbourhood to classify the edges. Furthermore, a maximum likelihood technique is used to estimate and remove the bias in the denoised images. The proposed method is compared against other existing methods and validated for both simulated as well as real ultrasound images. The proposed method outperforms other state-of-the-art methods in terms of qualitative and quantitative analysis. © 2016 IEEE.