Results 11 to 20 of about 28,366 (229)
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 deconvolution and phase retrieval
, 1988 Theoretical and practical aspects of identifying and deconvolving a convolution in more than one-dimension are presented. In contrast to conventional techniques which require knowledge of the blurring function, this thesis describes techniques for "blind" deconvolution.
R. Lane
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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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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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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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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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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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