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Image registration is amongst the most prominent problems in image processing and computer vision. Particularly in biomedical applications, automated alignment of image data from different imaging modalities has received great attention, delivering a high value added for analysis and diagnosis by integrating spatial information of two or more assays. In this context, the use of entropy based mutual information between images has been widely propagated to capture the relation between differential intensity distributions. In this work we address the problem of matching two different intensity distributions in a supervised learning scenario: We approximate a function relating both intensity distributions using a regression neural network predicting intensity values of one modality to the other, thereby allowing direct intensity difference registration. Predictions are based on a Gabor space representation of the input image, in order to capture local image structures. In experiments we show that the approach is i) able to learn a function to predict intensity values and ii) the predictions can be used to correctly register images by direct intensity differences minimization. The latter has the advantage of being computationally appealing and more stable concerning the optimization framework, which we exploit in registering histological section and NMRi data of plant specimen.