Blind Deconvolution Based on Correlation Spectral Negentropy for Bearing Fault [PDF]
Entropy, 2023 Blind deconvolution is a method that can effectively improve the fault characteristics of rolling bearings. However, the existing blind deconvolution methods have shortcomings in practical applications.
Tian Tian +3 more
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Sparse Blind Deconvolution with Nonconvex Optimization for Ultrasonic NDT Application [PDF]
Sensors, 2020 In the field of ultrasonic nondestructive testing (NDT), robust and accurate detection of defects is a challenging task because of the attenuation and noising of the ultrasonic wave from the structure.
Xuyang Gao +4 more
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Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images [PDF]
Journal of Imaging, 2021 The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium in which the light travels from the object to the final imaging sensor.
Francisco J. Ávila +4 more
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Adaptive blind deconvolution decomposition and its application in composite fault diagnosis of rolling bearings [PDF]
Scientific ReportsThe detection of rolling bearing faults is essential to ensure the operational safety of rotating machinery. An effective method for diagnosing rolling bearing faults is the maximum second-order cyclostationarity blind deconvolution (CYCBD) method, which
Wei Dong +3 more
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Blind Deconvolution for Ultrasound Sequences Using a Noninverse Greedy Algorithm [PDF]
International Journal of Biomedical Imaging, 2013 The blind deconvolution of ultrasound sequences in medical ultrasound technique is still a major problem despite the efforts made. This paper presents a blind noninverse deconvolution algorithm to eliminate the blurring effect, using the envelope of the ...
Liviu-Teodor Chira +3 more
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Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network [PDF]
SensorsThis paper proposes a novel approach to predicting the useful life of rotating machinery and making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural network.
Yangde Gao, Zahoor Ahmad, Jong-Myon Kim
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Spherical Aberration and Scattering Compensation in Microscopy Images through a Blind Deconvolution Method [PDF]
Journal of ImagingThe optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium the light passes while travelling from the object to the image plane. The computation of the point spread function (PSF)
Francisco J. Ávila, Juan M. Bueno
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Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution [PDF]
Sensors, 2019 Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due ...
Dominique Martinez +2 more
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Blind Hierarchical Deconvolution [PDF]
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020 Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement. Nevertheless, the majority of model-based inversion techniques require knowledge on the convolution kernel to recover an accurate reconstruction and additionally prior assumptions on the regularity of the ...
Arjas, A. (A.) +3 more
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IEEE Access, 2021 This work discusses a variational Bayesian learning approach towards decentralized blind deconvolution of seismic signals within a sensor network. Blind seismic deconvolution is cast into a probabilistic framework based on Sparse Bayesian learning ...
Dmitriy Shutin, Ban-Sok Shin
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