A Method for Image Reduction Based on a Generalization of Ordered
  Weighted Averaging Functions
  In this paper we propose a special type of aggregation function which
generalizes the notion of Ordered Weighted Averaging Function - OWA. The
resulting functions are called Dynamic Ordered Weighted Averaging Functions ---
DYOWAs. This generalization will be developed in such way that the weight
vectors are variables depending on the input vector. Particularly, this
operators generalize the aggregation functions: Minimum, Maximum, Arithmetic
Mean, Median, etc, which are extensively used in image processing. In this
field of research two problems are considered: The determination of methods to
reduce images and the construction of techniques which provide noise reduction.
The operators described here are able to be used in both cases. In terms of
image reduction we apply the methodology provided by Patermain et al. We use
the noise reduction operators obtained here to treat the images obtained in the
first part of the paper, thus obtaining images with better quality.
