Abstract
In this paper we present a multi-scale method for the detection of small targets embedded in noisy background. The multi-scale representation is built using a weighted undecimated discrete wavelet transform. The method, in essence, is based on the maximisation of information available at each resolution level of the representation. We show that such objective can be achieved by maximising Renyi’s information. This approach allows us to determine an adaptive threshold useful for discriminating, at each scale, between wavelet coefficients representing targets and those representing background noise. Eventually, avoiding inverse transformation, scale-dependent estimates are combined according to a majority vote strategy. The proposed technique is experimented on a standard data set of mammographic images.
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