Results 11 to 20 of about 25,670 (189)
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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Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2013 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Almeida +39 more
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Cells, 2021 To investigate the cellular structure, biomedical researchers often obtain three-dimensional images by combining two-dimensional images taken along the z axis. However, these images are blurry in all directions due to diffraction limitations.
Boyoung Kim
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Compressive Blind Image Deconvolution [PDF]
IEEE Transactions on Image Processing, 2013 We propose a novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images. The proposed framework relies on a constrained optimization technique, which is solved by a sequence of unconstrained sub-problems, and allows the incorporation of existing CS reconstruction algorithms
Bruno, Amizic +3 more
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Blind Deconvolution with Scale Ambiguity
Applied Sciences, 2020 Recent years have witnessed significant advances in single image deblurring due to the increasing popularity of electronic imaging equipment. Most existing blind image deblurring algorithms focus on designing distinctive image priors for blur kernel ...
Wanshu Fan +3 more
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Deep learning for blind structured illumination microscopy
Scientific Reports, 2022 Blind-structured illumination microscopy (blind-SIM) enhances the optical resolution without the requirement of nonlinear effects or pre-defined illumination patterns.
Emmanouil Xypakis +5 more
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Blind signal deconvolution based on pulsed neuron model [PDF]
ITM Web of Conferences, 2019 In this paper, we consider the vector-matrix model of a pulsed neuron, focused on solving problems of digital signal processing. We extend the application domain of the model to the blind signal deconvolution problem.
Bondarev Vladimir
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IEEE Transactions on Signal Processing, 2019 We introduce a novel multichannel blind deconvolution (BD) method that extracts sparse and front-loaded impulse responses from the channel outputs, i.e., their convolutions with a single arbitrary source. A crucial feature of this formulation is that it doesn't encode support restrictions on the unknowns, unlike most prior work on BD. The indeterminacy
Pawan Bharadwaj +2 more
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Research on Fault Extraction Method of CYCBD Based on Seagull Optimization Algorithm
Shock and Vibration, 2021 Maximum cyclostationarity blind deconvolution (CYCBD) can recover the periodic impulses from mixed fault signals comprised by noise and periodic impulses. In recent years, blind deconvolution has been widely used in fault diagnosis.
Qianqian Zhang +4 more
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