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- W1860581730 abstract "The principle of the method is to split the kernel into two secondary kernels : r(t)=k(t)+d(t), where d(t) must be invertible and satisfy a convergence condition. Then the deconvolution problem is to solve the following equation : i(t) = i_{o}(t)-int_{T} i(tau)g(t-tau)dtau where T is the signal support, i_{o}(t)=d^{*-1}(t) * o(t) and g(t)=d^{*-1}(t)*k(t) . This equation is solved by using successive substitutions. The deconvolution algorithm may be two steps or iterative and gives a super-resolution. Only the iterative form has been experimented. A noise free restoration of two pulses shows the validity of the method and the convergence speed with different splitting modes. Finally deconvolution from noisy data is studied." @default.
- W1860581730 created "2016-06-24" @default.
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- W1860581730 date "2005-03-24" @default.
- W1860581730 modified "2023-09-23" @default.
- W1860581730 title "Kernel splitting method in support constrained deconvolution for super-resolution" @default.
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- W1860581730 doi "https://doi.org/10.1109/icassp.1982.1171401" @default.
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