Seismic deconvolution is associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper, we address the problem of multichannel semiblind seismic deconvolution,
This paper presents rank-awareness algorithms to solve sparse blind deconvolution using modulated input. We consider sparse blind deconvolution as a rank-one column-sparse matrix recovery problem, so the proposed algorithms can use both the rank-one property and the sparsity of the unknowns. Unknown...
In the next section, we overview some of the prior works on multichannel techniques for speech separation. The signal of individual sources can be recovered through multichannel linear filtering. These techniques can be grouped in two categories: independent component analysis and beamforming. The ...
sparse blind deconvolutionThe sound absorption of materials is traditionally measured in laboratory condition with one of two methods: random incidence in a reverberant chamber (ISO 354) or normal incidence in an impedance tube (ISO 10534). Nevertheless, there are some materials that cannot be ...
We propose a multichannel sparse spike deconvolution method with a sparsity-promoting constraint and an extra lateral constraint exploiting the spatial relationships among adjacent seismic traces. Firstly, the dynamic time warping (DTW) is performed between any two adjoining seismic traces to obtain the ...
blind image deconvolutionnonnegative matrix factorizationsparse matrix factorizationmultichannel image deconvolutionsparseness constraintsmixing vectornonsparse source imageNovel approach to single frame multichannel blind image deconvolution has been formulated recently as non-negative matrix factorization problem with ...
Seismic deconvolution encounters problems of multiplicity and instability. The regularization technique based on various prior knowledge is used to constrain the range of solutions and improve the stability of deconvolution results. We use dictionary learning as a technical means to consider both vertical ...
super-Gaussian densities are natural generalizations of Gaussian densities, and permit the derivation of monotonic iterative algorithms for parameter estimation in sparse coding in overcomplete signal dictionaries, blind source separation, independent component analysis, and blind multichannel deconvolution. Mixtur...
Simultaneous total variation image inpainting and blind deconvolution. Int. J. Imaging Syst. Technol. 2005, 15, 92–102. [CrossRef] 24. Criminisi, A.; Perez, P.; Toyama, K. Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 2004, 13, 1200–...