Cross Spectral Image Reconstruction Using a Deep Guided Neural Network

Frank Sippel, Jürgen Seiler, André Kaup

Abstract: Cross spectral camera arrays, where each camera records different spectral content, are becoming increasingly popular for RGB, multispectral and hyperspectral imaging, since they are capable of a high resolution in every dimension using off-the-shelf hardware. For these, it is necessary to build an image processing pipeline to calculate a consistent image data cube, i.e., it should look like as if every camera records the scene from the center camera. Since the cameras record the scene from a different angle, this pipeline needs a reconstruction component for pixels that are not visible to peripheral cameras. For that, a novel deep guided neural network (DGNet) is presented. Since only little cross spectral data is available for training, this neural network is highly regularized. Furthermore, a new data augmentation process is introduced to generate the cross spectral content. On synthetic and real multispectral camera array data, the proposed network out-performs the state of the art by up to 2 dB in terms of PSNR on average. Besides, DGNet also tops its best competitor in terms of SSIM as well as in runtime by a factor of nearly 12. Moreover, a qualitative evaluation reveals visually more appealing results for real camera array data.