CERN-HEP-Challenge
The hybrid challenge blending Quantum Computing and High-Energy Physics to push the boundaries of science.
Hosted by
CERN
Posted
Adaptive filter for Jcnoise Wiener Wt
Prof. Asis. Sr. Juan José Molina González
University of Murcia
Stochastics Wiener grayscale modulation decay in Mathlab for Z-transform
For an grayscale wiener 2 decay in Saturn additive noise in the Sum. of
“bar”(the values of stochastics Wiener adaptative filter throuth are
building the Hdi signal) requires for the modulation respect the size
noise and taken the values of Oi[n m] Iters in ON/OFF for the values
Oj[m n] in the deconvolution of Sum. Saturn additive for the mixture of
size noir. The process building the IO for the sintax are following:
J = wiener2(I,[m n],noise) lters the grayscale image I using a pixel-wise adaptive
low-pass Wiener lter in this sintaxis for adaptative[m n] species the size (m-by-n) of
the neighborhood used to estimate the local image in Z-transform for
N/length{Gaussian} over the standard deviation. The additive noise (Gaussian white
noise) power is assumed to be noise.
The input image has been degraded by constant power additive noise. wiener2 uses a
pixelwise adaptive Wiener method based on statistics estimated from a local
neighborhood of each pixel.
[J,noise_out] = wiener2(I,[m n]) returns the estimates of the additive noise
power wiener2 calculates before doing the ltering. Handsome the Iter degray in an bar
for local [J,noise_out] and higuer in the same bar for [J.noise,on] The Wiener lter
tailors itself to the local image variance. Where the variance is large, wiener2 performs
little smoothing. Where the variance is small, wiener2 performs more smoothing.
This approach often produces better results than linear ltering. The adaptive lter is
more selective than a comparable linear lter, preserving edges and other high-
frequency parts of an image. In addition, there are no design tasks; the wiener2
function handles all preliminary computations and implements the lter for an input
image. wiener2, however, does require more computation time than linear ltering.
wiener2 works best when the noise is constant-power ("white") additive noise, such
as Gaussian noise. The example below applies wiener2 to an image of Saturn with
added Gaussian noise.
Order by: