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- W2109180568 abstract "In this paper, we tackle the problem of image denoising. Given an image distorted by additive noise of unknown probability distribution, the intensity difference between the distorted and the original unknown images must be bounded. This idea is exploited here and we design a series of weighted adaptive local bounds based on local intensity information, such as the mean, variance, median and etc. The proposed method is tested and compared with other standard techniques, such as wavelet thresholding, for image denoising. Apart from the simplicity of implementation, the results are very encouraging, as far as both visual quality of the denoised images and quantitative metrics of improvement are concerned. More importantly it provides simultaneous denoising of mixed noise, which is not obtainable by using single conventional denoising methods." @default.
- W2109180568 created "2016-06-24" @default.
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- W2109180568 date "2006-10-11" @default.
- W2109180568 modified "2023-09-26" @default.
- W2109180568 title "An Improved Image Denoising Algorithm based on Weighted Adaptive Local Bounds" @default.
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- W2109180568 doi "https://doi.org/10.1109/icassp.2005.1415335" @default.
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