A deep learning based spatial dependency modelling approach towards super-resolution

Super-resolution techniques use subpixel information to predict high resolution classification maps from coarse images. This study investigates for an unsupervised super-resolution approach which considers the image features to predict target spatial dependencies. Novelty of the approach is that the convolution neural networks and deep autoencoders are explored in this context. Evaluation over standard datasets revealed that the proposed method is more effective than the state of art unsupervised approaches. The method is also found to be preferable over variogram based approaches for complex scenes. This study also compares the effectiveness of shallow and deep networks and investigates the possible assessment of the optimal depth for the learning network. This technique can be further extended to a supervised framework.