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In the mind of the public, I obviously work at a certain noisy level.
where ϵ > 0 is a bound on the noisy level.
The final extracted road map in all noisy level is almost equal to noiseless result.
where the recovered function f is compactly supported and twice continuously differentiable away from a smooth edge; W denotes a Wiener sheet; ε is a noisy level.
Suppose (g^{epsilon }) is for measured data in (L^{2}(0,2pi)) and satisfies begin{aligned} bigl| g^{epsilon }-gbigr| _{L^{2}(0,2pi)} le epsilon, end{aligned} (2) where (epsilon >0) represents a noisy level.
Figures 1-4 show the comparison between the exact solution and its computed approximations for different values N (:= number of grid points), k (:= number of iterations), ω (:= relaxation factor), ε (:= noisy level) and (E_{r}(f)= frac{|f_{mathrm{approximate}}-f_{mathrm{exacte}}|_{|f_{mathrm{exacte}}|_) (:= relative error).
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We see that the approximations are acceptable for both interior and boundary temperature, and the numerical results are stable with the increase of noisy levels.
In plot b, the noisy speech level estimate A k (n) (solid) and the noise-level estimates B k (n) (dotted), corresponding to the subband gains in plot a, are shown.
Figures 1 6 show the denoising results of the six noisy gray level images by different methods.
Table 1 lists the PSNR, ISNR, and ReErr results by different methods on the six noisy gray level images.
The noisy speech level is estimated by taking a short-time average of the input signal according to A k ( n ) = α k A k ( n - 1 ) + 1 - α k | x k ( n ) |, (4).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com