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Non-linear aggregation of filters to improve image denoising (1904.00865v3)
Published 1 Apr 2019 in stat.ML, cs.CV, cs.LG, and eess.IV
Abstract: We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. We provide a theoretical bound to support our aggregation scheme, its numerical performance is illustrated and we show that the aggregate significantly outperforms each of the preliminary filters.
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