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- W4313444992 abstract "The study and development of image noise removal algorithm (INRA) are very important research area, where the goal is to remove unwanted noise from an image such that maximum significant information can be observed in noise removed image. Peak-signal-to-noise-ratio (PSNR) and execution time (ET) are the main criteria for INRA evaluation. In this paper, ridge regression models of degree three have been developed for prediction of PSNR of recursive median filter-based INRA without its implementation. Prediction of PSNR of INRA by regression model is a novel contribution which helps to save ET. Accurate prediction is a challenging problem due to randomness in noise, images, and sampled data. Theory based on Bias Variance Decomposition Theorem (BVDT) and minimum testing error has been used to select ‘good’ regression models. Experiments ensure computation time advantage by a factor of $$10^{-5}$$ at the cost of 1% and 10% error in PSNR values of the database with one and ten images, respectively." @default.
- W4313444992 created "2023-01-06" @default.
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- W4313444992 date "2023-01-01" @default.
- W4313444992 modified "2023-10-16" @default.
- W4313444992 title "Ridge Regression for PSNR of Restored Images by Recursive Median Filter" @default.
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- W4313444992 doi "https://doi.org/10.1007/978-981-19-2358-6_44" @default.
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