When interpolating images in the wavelet domain, the main problem is how to estimate the finest detail coefficients. Wavelet coefficients across scales have an interscale dependency, and the dependency varies according to the local energy of the coefficients. This implies the possible existence of functional mappings from one scale to another scale. If we can estimate the mapping parameters from the observed coefficients, then it is possible to predict the finest detail coefficients. In this article, we use the multilayer perceptron (MLP) neural networks to learn a mapping from the coarser scale to the finer scale. When exploiting the MLP neural networks, phase uncertainty, a well-known drawback of wavelet transforms, makes it difficult for the networks to learn the interscale mapping. We solve this location ambiguity by using a phase-shifting filter. After the single-level phase compensation, a wavelet coefficient vector is assigned to one of the energy-dependent classes. Each class has its corresponding network. In the simulation results, we show that the proposed scheme outperforms the previous wavelet-domain interpolation method as well as the conventional spatial domain methods.
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