`noise`¶

`supreme.noise.dwt_denoise`(X[, wavelet, ...])	Denoise an image using the Discrete Wavelet Transform.
`supreme.noise.variance`

dwt_denoise¶

supreme.noise.dwt_denoise(X, wavelet='db8', levels=4, alpha=2)¶

Denoise an image using the Discrete Wavelet Transform.

Parameters:

Parameters:	X : ndarray of uint8 Image to denoise. wavelet : str Wavlet family to use. See supreme.lib.pywt.wavelist() for a complete list. levels : int Number of levels to use in the decomposition. alpha : float Parameter used to tweak the Wiener estimator. A larger value of alpha results in a smoother output.
Returns:	Y : ndarray of float64 Denoised image.

X : ndarray of uint8

Image to denoise.

wavelet : str

Wavlet family to use. See supreme.lib.pywt.wavelist() for a complete list.

levels : int

Number of levels to use in the decomposition.

alpha : float

Parameter used to tweak the Wiener estimator. A larger value of alpha results in a smoother output.

Returns:

Y : ndarray of float64

Denoised image.

Notes

Implemented according to the overview of [R8] given in [R7].

References

[R7]	(1, 2) J. Fridrich, “Digital Image Forensics,” IEEE Signal Processing Magazine, vol. 26, 2009, pp. 26-37.

[R8]	(1, 2) M. Mihcak, I. Kozintsev, K. Ramchandran, and P. Moulin, “Low-complexity image denoising based on statistical modeling of wavelet coefficients,” IEEE Signal Processing Letters, vol. 6, 1999, pp. 300-303.