\section{Conclusion
	\label{section:conclusion} }


This detailed study carried out on NL-Bayes had led us to the following conclusions:
\begin{itemize}
	\vspace{-3mm}\item According that we work on very small size of patches,
this method is really fast for small and medium noise, even more thanks to the
trick that used patches are no more processed again as reference patches;
	\vspace{-3mm}\item The main elements which allow for real improvements of
the results are:
	\begin{itemize}
		\vspace{-3mm}\item working in the $Y_0U_0V_0$ space color for the
first step;
		\vspace{-3mm}\item making a second step with the result of the
first step as ``oracle'';
		\vspace{-3mm}\item aggregating the  estimates. This aggregation is
improved significantly by working with a 3D group and by keeping all
estimates obtained of the similar 2D-patches like BM3D;
		\vspace{-3mm}\item using the homogeneous area criteria to remove
almost all artifacts in homogeneous area.
	\end{itemize}
	\vspace{-3mm}\item Nevertheless,  there is still room for improvement, by,
perhaps:
	\begin{itemize}
		\vspace{-3mm}\item using a multi-scale approach to remove low
frequency noise;
		\vspace{-3mm}\item using a more complex model than the Gaussian
patch model.
	\end{itemize}
\end{itemize}
