  Monotonicity with respect to all arguments is fundamental to the definition
of aggregation functions. It is also a limiting property that results in many
important non-monotonic averaging functions being excluded from the theoretical
framework. This work proposes a definition for weakly monotonic averaging
functions, studies some properties of this class of functions and proves that
several families of important non-monotonic means are actually weakly monotonic
averaging functions. Specifically we provide sufficient conditions for weak
monotonicity of the Lehmer mean and generalised mixture operators. We establish
weak monotonicity of several robust estimators of location and conditions for
weak monotonicity of a large class of penalty-based aggregation functions.
These results permit a proof of the weak monotonicity of the class of
spatial-tonal filters that include important members such as the bilateral
filter and anisotropic diffusion. Our concept of weak monotonicity provides a
sound theoretical and practical basis by which (monotone) aggregation functions
and non-monotone averaging functions can be related within the same framework,
allowing us to bridge the gap between these previously disparate areas of
research.
