in the second step, an appropriate filter, designed to reduce the Gaussian noise, is used. Although frequently successful, this strategy has severe drawbacks. The effectiveness of
which is an effective method of reducing the impact of noise-induced distortions that can smooth out the noise while preserving image edges. Owing to the ability of MS to flatten the color images, it is also used as a pre-processing stage in image segmentation tasks. This method works succes...
The performance of LT codes over the AWGN channel is also investigated in [50]: the ensemble weight distribution of LT codes is derived and maximum likelihood decoding performance is upper-bounded by a refined version of the union bound. Show moreView chapter Book 2014, Academic Press Library ...
Likelihood 4.6 Gaussian models In this section, we consider modeling each sequence {si} of source signals as a realization of a zero-mean Gaussian vector with a T×T covariance matrix Ri=E{siTsi}. It is not possible to estimate all the T(T+1)/2 free parameters of the covariance matrice...
The non€ Gaussianity constraint is necessary to distinguish between causal€ invertible and non€ causal/non€ invertible models. Many of the existing estimation procedures adopt quasi€ likelihood methods by assuming a non€ Gaussian density function for the noise distribution that is fully known up...
Maximum likelihood estimation We estimate the parameters of the kernel using Maximum Likelihood Estimation (MLE). The aim is to find the parameter values that maximize the likelihood function, given the SLC predicted by WAVI. The likelihood function measures how probable the obtained SLC are, given...
a maximum likelihood estimator. Various ways of improving the efficiency of NLM were proposed in Goossens et al.86. Especially the influence of the choice of the weighting function on the noise smoothing efficiency was investigated. In87, the NLM has been combined with the region homogeneity ...
Specifically, the Gaussian likelihood term is added to the unsupervised rain residue to simulate the real rain pattern, and the TV regularization term is added to the background part. The similarity between the real rain residue image and the KL divergence is further synthesized by using the KL...
To implement the GP regression model, all hyperparameters θ contained in the covar- iance matrices, along with the noise power, must first be determined. A common approach is to use Type II Maximum Likelihood (ML-II), which fixes the hyperparame- ters at their respective Maximum Likelihood...
The nature of this assumption cannot be understated, as Rasmussen and Williams [43] explain: this noise assumption, together with the model, explicitly gives rise to the GP likelihood model—see Ref. [43] for details. Under a different model, we would not receive this simple likelihood model...