A Multispectral Denoising Framework for Seismic Random Noise Attenuation

Lin, Yan-Bo; Zhang, Jinhai

doi:10.1109/tgrs.2020.3047633

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…Other RL applications in image denoising have been demonstrated in [25], [26]. In [27], Chao Liang et al have used an asynchronous advantage actor-critic (A3C) agent to dynamically alter the denoiser to denoise complex and diverse seismic signals. In [28], hand gesture recognition using sEMG signals is achieved with deep Q-learning and double deep Q-learning.…”

Section: Introductionmentioning

confidence: 99%

supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal Decontamination

Jena,

Gehlot,

Kumar

et al. 2024

IEEE Access

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The presence of muscles throughout the active parts of the body, such as the upper and lower limbs, makes electromyography-based human-machine interaction prevalent. However, muscle signals are stochastic and noisy, with noises being both regular and irregular. Irregular noises due to movements or electrical switching require dynamic filtering. Conventionally, filters are stacked, which unnecessarily trims and delays the signal. This study introduces a decontamination technique involving a supervised rewarding strategy to drive a deep Q-network-based agent (supDQN). It applies one of three filters to decontaminate a 1 sec long surface electromyography signal, which is dynamically contaminated. A machine learning agent identifies whether the signal after filtering is clean or noisy, generating a reward accordingly. The identification accuracy is enhanced by using a local interpretable model-agnostic explanation. The deep Q-network is guided by this reward to select the filter optimally while decontaminating a signal. The proposed filtering strategy is tested on four noise levels (-5 dB, -1 dB, +1 dB, +5 dB). supDQN filters the signal desirably when the signal-tonoise ratio (SNR) is between -5 dB to +1 dB but filters less desirably at high SNR (+5 dB). A normalized root mean square (Ω) is formulated to depict the difference of the filtered signal from the ground truth. This is used to compare supDQN and conventional methods, including wavelet denoising with debauchies and symlet wavelet, high-order low-pass filter, notch filter, and high-pass filter. The proposed filtering strategy gives an average Ω value of 1.1974, which is lower than that of the conventional filters.

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Section: Introductionmentioning

confidence: 99%

supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal Decontamination

Jena,

Gehlot,

Kumar

et al. 2024

IEEE Access

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“…In recent years, the matrix CUR (MCUR) method [31][32][33] for fast low-rank approximation of real matrices has been actively investigated because of its ability to efficiently handle large-scale problems. The MCUR method approximates a low-rank matrix by directly sampling a subset of columns and rows from the original matrix and representing it as a product of three small-scale matrices.…”

Section: Introductionmentioning

confidence: 99%

Efficient quaternion CUR method for low-rank approximation to quaternion matrix

Wu,

Kou,

Cai

et al. 2024

Preprint

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The low-rank quaternion matrix approximation has been successfully applied in many applications involving signal processing and color image processing. However, the cost of quaternion models for generating low-rank quaternion matrix approximation is sometimes considerable due to the computation of the quaternion singular value decomposition (QSVD), which limits their application to real large-scale data. To address this deficiency, an efficient quaternion matrix CUR (QMCUR) method for low-rank approximation is suggested , which provides significant acceleration in color image processing. We first explore the QMCUR approximation method, which uses actual columns and rows of the given quaternion matrix, instead of the costly QSVD. Additionally, two different sampling strategies are used to sample the above-selected columns and rows. Then, the perturbation analysis is performed on the QMCUR approximation of noisy versions of low-rank quater-nion matrices. Extensive experiments on both synthetic and real data further reveal the superiority of the proposed algorithm compared with other algorithms for getting low-rank approximation, in terms of both efficiency and accuracy.

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