Table 9 shows the performance of proposed algorithm for multiple biometric watermark images under JPEG compression and Gaussian noise
In this experiment, instead of using only a white Gaussian noise
a correlated signal is used to excite both the adaptive filter and unknown structure which is generated by the following model:
1) Gaussian noise
turned into discrete, homogeneous distribution after bi-spectrum analysis, whose influence can be basically eliminated;
With the increase of Gaussian noise
variance, the extent of decline rate of SRC is greater than that of our method.
Unfortunately, the Xue method is far from satisfactory under the conditions of both Gaussian noise
and Salt and pepper noise.
Recovering the original image from a degraded image with additive white Gaussian noise
can be modelled by solving the ill-posed system Y = X + v, where Y denotes the degraded image, X denotes the original image, and v denotes the noise term.
The results show that the method proposed has a good resistance to many image attacks such as Gaussian noise
After a short review on signal detection under Gaussian noise
in [section]2, in [section]3 we incorporate small deviation from Gaussian distribution in terms of the Edgeworth expansion and calculate the likelihood ratio with it.
The noise models we consider are the additive white Gaussian noise
(AWGN) and the additive white uniform noise (AWUN), whose probability density functions are, respectively,
The background noise has been modelled as white Gaussian noise
with PSD of -140 dBm/Hz.
There is an important feature that the higher order cumulants (greater than second order) of Gaussian noise
are permanent zero.
It is worth to point out that the diffuse reflection component is treated as the incoherent white Gaussian noise
, for simplicity .