Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item 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.Item Extending Denoising AutoEncoders for Feature Recognition(Institute of Electrical and Electronics Engineers Inc., 2018) Jeppu, N.; Chandrasekaran, K.Image rendering techniques involve the addition of noise on the rendered samples. The characteristics of Monte Carlo rendered noisy images makes it difficult to denoise using conventional methods. The noise induced in this case is spatially sparse. Using traditional methods of denoising and feature recognition is time consuming for such images as they use the entire image space as their search space. This results in unnecessary calculations that can be avoided and therefore reduce the processing time significantly. A recurrent convolutional neural network that operates on varying spatial resolutions, also known as the auto encoder decoder structure perform very well on these rendered images. The partitioning into encoder and decoder phases lets the network operate on continuously decreasing and increasing spatial domains respectively. This can be coupled with classification techniques to incorporate feature recognition of noisy images. In this paper the pretrained autoencoder layers are supported with additional softmax and sigmoid layers to enable feature recognition capabilities. © 2018 IEEE.Item Comparison of pre-processing filters on the performance of sEMG based pattern recognition(Institute of Electrical and Electronics Engineers Inc., 2019) Powar, O.S.; Chemmangat, K.The noise present in the surface electromyography (sEMG) signals is a significant problem in the control of the rehabilitation scheme. Different noise reducing methods have been discussed and considered individually in previous studies. However, there is limited work on the comparison of different noise reduction strategies. To achieve good performance of Myoelectric Control (MEC) system, the selection of filters becomes essential. The vital contribution of this work is a study into the comparison of three denoising methods including Butterworth filter, Weiner filter and Spectral Subtraction (SS) filter that has been used to remove the noise from sEMG signal. Performance evaluation of the three noise reduction methods is done regarding classification accuracy and computation time. The three denoising methods have been validated on the recorded sEMG of seven healthy subjects while performing eight classes of movements from the two muscle positions on the right forearm. The accuracy is compared with four classifiers namely, J48, k-nearest neighbors (KNN), Naive Bayes and Linear Discriminant Analysis (LDA). Results show that the Butterworth filter provides marginally better performance than the other two filters regarding classification accuracy; when computation time is considered SS filter offers significant savings. A visual inspection of the output of the Weiner filter hints at its utility as a muscle activity onset detection tool. © 2019 IEEE.Item 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.Item Performance Analysis of VMD to Decompose, Detrend and Denoise Power System Signals(Institute of Electrical and Electronics Engineers Inc., 2024) Rathod, N.S.; Shubhanga, K.N.Variational Mode Decomposition (VMD) has gained significant attention as an effective tool for signal processing, particularly in the fields of biomedical and speech processing. This paper explores the application of VMD to decompose complex power system signals which are non-stationary and nonlinear. Standard Empirical Mode Decomposition (EMD) and its variants often encounter challenges like mode mixing, boundary problems, and parameter dependency on noise levels, which may adversely affect the accuracy and reliability of the decomposition results. Since VMD effectively addresses these challenges by providing a more robust framework for decomposition, the resulting Intrinsic Mode Functions (IMFs) have been successfully used for mode estimation, detrending and denoising of power system signals. While denoising, to automate the process of identifying noisy IMFs reliably, Noise Identification Indices (NIIs) have been used. This study employs datasets from 3-machine, 9-bus power system and real-world ISO New England (ISO-NE) power system signals to demonstrate the efficacy and applicability of VMD in practical scenarios. These findings show up the potential application of VMD for analysing power system signals to advance signal processing techniques across various fields. © 2024 IEEE.