Conference Papers

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

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Author: search.filters.author.Rajan, J.Subject: search.filters.subject.Denoising

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    A hybrid model for rician noise reduction in MRI
    (IEEE Computer Society help@computer.org, 2013) Sudeep, P.V.; Ponnusamy, P.; Rajan, J.
    Magnetic Resonance Images (MRI) are normally corrupted with random noise mainly arised from the patient's body and from the scanning apparatus. This paper describes a new technique to remove the homogeneous Rician noise in the magnitude magnetic resonance (MR) images. Linear minimum mean square error (LMMSE) estimator is a good choice to solve this inverse problem. In another way, denoising can be considered as a solution for L1 regularization problem of compressed sensing (CS). The Split Bregman iteration technique is effectively used in this stage in order to minimize the total variation (TV) functional. By combining these results in transform domain, the denoising is expected to be improved. Experiments show that the proposed algorithm outperforms other existing methods in the literature in terms of Peak Signal to Noise Ratio (PSNR). © 2013 IEEE.
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    A Novel Deep Learning Approach for the Removal of Speckle Noise from Optical Coherence Tomography Images Using Gated Convolution–Deconvolution Structure
    (Springer Science and Business Media Deutschland GmbH, 2020) Menon, S.N.; Vineeth Reddy, V.B.; Yeshwanth, A.; Anoop, B.N.; Rajan, J.
    Optical coherence tomography (OCT) is an imaging technique widely used to image retina. Speckle noise in OCT images generally degrades the quality of the OCT images and makes the clinical diagnosis tedious. This paper proposes a new deep neural network despeckling scheme called gated convolution–deconvolution structure (GCDS). The robustness of the proposed method is evaluated on the publicly available OPTIMA challenge dataset and Duke dataset. The quantitative analysis based on PSNR shows that the results of the proposed method are superior to other state-of-the-art methods. The application of the proposed method for segmenting retinal cyst from OPTIMA challenge dataset was also studied. © 2020, Springer Nature Singapore Pte Ltd.