Blind deconvolutionampLTS2—Multiple images of the same scene improve the result of multichannel blind deconvolution problem. However, existing techniques require alignment of channels. We define a multichannel deconvolution scheme for highly correlated images to estimated theHosseini Kamal, Mahd...
For more than half a century, seismic deconvolution has been of a great interest in reflection seismics. Its goal is to remove the effect of the seismic wavelet, i.e., the waveform produced by the seismic source, from the data. Its importance to the industry is that it increases the res...
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 ...
Geophysical data interpolation has attracted much attention in the past decades. While a variety of methods are well established for either regularly sampled or irregularly sampled multi-channel data, an effective method for interpolating extremely sparse data samples is still highly demanded. In this p...
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 study the multi-channel sparse blind deconvolution (MCS-BD) problem, whose task is to simultaneously recover a kernel a and multiple sparse inputs {x_i}_(i=1)~p from their circulant convolution y_i = a * x_i (i = 1, ..., p). We formulate the task as a nonconvex ...
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 ...
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...
A blind nonstationary deconvolution method for multichannel seismic data convolution model, we propose to formulate the objective function for reflectivity inversion as a joint low-rank and sparse inversion convex optimisation ... Y Jiang,S Cao,S Chen,... - 《Exploration Geophysics》 被引量: 0发...