Results 11 to 20 of about 323,867 (242)
Blind deconvolution of video sequences [PDF]
2008 15th IEEE International Conference on Image Processing, 2008 We present a new blind deconvolution method for video sequence. It is derived following an inverse problem approach in a Bayesian framework. This method exploits the temporal continuity of both object and PSF Combined with edge-preserving spatial regularization, a temporal regularization constrains the blind deconvolution problem, improving its ...
Ferréol Soulez +4 more
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Complete Blind Subspace Deconvolution [PDF]
, 2009 In this paper we address the blind subspace deconvolution (BSSD) problem; an extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. While previous works have been focused on the undercomplete case, here we extend the theory to complete systems.
Zoltán Szabó, Szabo, Z
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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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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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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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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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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 deconvolution and structured matrix computations with applications to array imaging
, 2017 © 2007 by Taylor & Francis Group, LLC. In this chapter, we study using total least squares (TLS) methods for solving blind deconvolution problems arising in image recovery.
Ng, Michael K., Plemmons, Robert J.
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Progressive Blind Deconvolution [PDF]
, 2015 We present a novel progressive framework for blind image restoration. Common blind restoration schemes first estimate the blur kernel, then employ non-blind deblurring. However, despite recent progress, the accuracy of PSF estimation is limited. Furthermore, the outcome of non-blind deblurring is highly sensitive to errors in the assumed PSF. Therefore,
Rana Hanocka, Nahum Kiryati
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Unrolled Compressed Blind-Deconvolution
IEEE Transactions on Signal Processing, 2023 Accepted to IEEE ...
Bahareh Tolooshams +3 more
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