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A new wavelet representation for multivalued images is presented. The idea for this representation is based on the first fundamental form that provides a local measure for the contrast of a multivalued image. In this paper, this concept is extended toward multiscale fundamental forms using the dyadic wavelet transform of Mallat (1992). The multiscale fundamental forms provide a local measure for the contrast of a multivalued image at different scales. The representation allows for a multiscale edge description of multivalued images. A variety of applications is presented, including multispectral image fusion, color image enhancement and multivalued image noise filtering. In an experimental section, the presented techniques are compared to single valued and/or single scale algorithms that were previously described in the literature. The techniques, based on the new representation are demonstrated to outperform the others.