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Learning visual co-occurrence with auto-encoder for image super-resolution

This paper proposes a novel neural network learning the essential mapping function between the low resolution and high resolution image for Image superresolution problem. In our approach, patch recurrence property of small patches in natural image are utilized as a prior to train the network. An autoencoder neutral network is designed to reconstruct the high resolution patches. The constraint that the output of the coding part should be similar as the corresponding high resolution patches is imposed to ameliorate the illness nature of the superresolution problem. In fact, the degeneration mapping from the high resolution image to the low resolution image is also integrated in the network. Both visual improvements and objective assessments are demonstrated on true images.