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• W2891961718 abstract "In this article we propose a new recursive video denoising method with high performance. The method is recursive and uses only the current frame and the previous denoised one. It considers the video as a set of overlapping temporal patch trajectories. Following a Bayesian approach each trajectory is modeled as linear dynamic Gaussian model and denoised by a Kalman filter. To estimate its parameters, similar patches are grouped and their trajectories are considered as sharing the same model parameters. The filtering is mainly temporal; non-local spatial similarity is only used to estimate the parameters. This temporally causal method obtains results comparable (in terms of PSNR and SSIM) to state-of-the-art methods using several frames per frame denoised, but with a higher temporal consistency." @default.
• W2891961718 created "2018-09-27" @default.
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• W2891961718 date "2018-10-01" @default.
• W2891961718 modified "2023-09-24" @default.
• W2891961718 title "Non-Local Kalman: A Recursive Video Denoising Algorithm" @default.
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• W2891961718 doi "https://doi.org/10.1109/icip.2018.8451421" @default.
• W2891961718 hasPublicationYear "2018" @default.
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