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|Title:||A fast algorithm for sparse multichannel blind deconvolution|
Takahata, André K.
Romano, João M. T.
|Abstract:||We have addressed blind deconvolution in a multichannel framework. Recently, a robust solution to this problem based on a Bayesian approach called sparse multichannel blind deconvolution (SMBD) was proposed in the literature with interesting results. However, its computational complexity can be high. We have proposed a fast algorithm based on the minimum entropy deconvolution, which is considerably less expensive. We designed the deconvolution filter to minimize a normalized version of the hybrid l(1)/l(2)-norm loss function. This is in contrast to the SMBD, in which the hybrid l(1)/l(2)-norm function is used as a regularization term to directly determine the deconvolved signal. Results with synthetic data determined that the performance of the obtained deconvolution filter was similar to the one obtained in a supervised framework. Similar results were also obtained in a real marine data set for both techniques|
|Editor:||Society of Exploration Geophysicists|
|Appears in Collections:||FEEC - Artigos e Outros Documentos|
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