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• W3093642711 abstract "Abstract Wavelet decompositions of integral operators have proven their efficiency in reducing computing times for many problems, ranging from the simulation of waves or fluids to the resolution of inverse problems in imaging. Unfortunately, computing the decomposition is itself a hard problem which is oftentimes out of reach for large-scale problems. The objective of this work is to design fast decomposition algorithms based on another representation called product-convolution expansion. This decomposition can be evaluated efficiently, assuming that a few impulse responses of the operator are available, but it is usually less efficient than the wavelet decomposition when incorporated in iterative methods. The proposed decomposition algorithms, run in quasi-linear time and we provide some numerical experiments to assess its performance for an imaging problem involving space-varying blurs." @default.
• W3093642711 created "2020-10-29" @default.
• W3093642711 creator A5038104386 @default.
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• W3093642711 date "2020-10-21" @default.
• W3093642711 modified "2023-10-06" @default.
• W3093642711 title "Fast wavelet decomposition of linear operators through product-convolution expansions" @default.
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• W3093642711 doi "https://doi.org/10.1093/imanum/draa072" @default.
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