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• W2095291130 startingPage "225" @default.
• W2095291130 abstract "In this paper, a novel framework is presented for the denoising of color video sequences corrupted by additive Gaussian noise. The proposed technique consists of three filtering stages: spatial, spatio-temporal, and spatial postprocessing. During the first spatial stage, the gradient values in eight directions for pixels located in the vicinity of a central pixel, as well as the interchannel correlation between the analogous pixels in different color bands (RGB), are taken into account. These gradient values that estimate the level of noise contamination are employed using the designed fuzzy rules to preserve the image features (e.g., textures, edges, sharpness, and chromatic properties). In the spatio-temporal denoising stage, two consecutive video frames are filtered together, thereby yielding more information. Additionally, small local motions between consecutive frames are estimated using block matching procedure in different directions, gathering interframe samples with similar features for efficient denoising. In the final stage, the edge and plain areas in a current frame are separated for different spatial postprocessing denoising. Two variants of proposed fuzzy filter, depending on sliding windows, are proposed. Additionally, a hybrid fuzzy-Wiener denoising technique is performed employing the proposed filtering approach. Numerous simulation results confirm that these novel fuzzy frameworks outperform other state-of-the-art techniques in terms of objective criteria, as well as subjective visual perception in the various color sequences." @default.
• W2095291130 created "2016-06-24" @default.
• W2095291130 creator A5018279114 @default.
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• W2095291130 date "2015-05-01" @default.
• W2095291130 modified "2023-10-18" @default.
• W2095291130 title "Fuzzy color video filtering technique for sequences corrupted by additive Gaussian noise" @default.
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• W2095291130 doi "https://doi.org/10.1016/j.neucom.2014.12.025" @default.
• W2095291130 hasPublicationYear "2015" @default.
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