The term fusion means in general an approach to combine the important information simultaneously from several sources (channels). When we approach image fusion, multiscale transforms (MST) are commonly used as the analyzing tool. It transforms the sources into a space-frequency domain which can be understood as a measure of the saliency (activity level). The criterion to fuse consists of taking the decision to preserve the most salient data from the sources. In order to reduce sensitivity against noise the saliency is often averaged over certain neighborhood (window). However averaging produces that decisions become more fuzzy. Traditionally the size of the neighborhood is chosen fixed according to the level of noise present in the sources, which has to be estimated in advance. This paper proposes a novel technique which combines a set of decreasing averaging windows in order to exploit the advantages of each one. We call it multisize windows-based fusion. This technique apart from improving fusion results avoids selecting the neighboring size in advance (and therefore to estimate the level of noise) since it only needs a simple set of windows defined according to image size. We compared it with another technique developed by us called oriented windows which, although it consider a fixed neighborhood, adapts the averaging shape to the spatial orientation of the saliency. The specific case of multifocus image fusion is considered for the experiments. The multisize windows technique delivers the best percentage of correct decisions compared with any single fixed window in all the experiments carried out, adding different noise sources (Gaussian, speckle and salt&pepper) with different levels. Although it does not performs better than the oriented window scheme one has to bear in mind that oriented windows are tuned in each case to the best size.