Variation learning guided convolutional network for image interpolation

In this paper, we propose a variational learning model that effectively exploits the structural similarities for image representation, and construct a deep network based on this model for image interpolation. Based on the local dependency, our learning model represents an image as the three-dimensional features. Besides two coordinate dimensions, an additional neighboring variation dimension is added to encode every pixel as the variation to its nearest low-resolution pixel by the local similarity. This added dimension lowers the risk of over-fitting for learning approaches and constructs abundant structural correspondences for inferring the missing information lost in image degradation. Then, this three-dimensional features are naturally modeled, extracted and refined by an end-to-end trainable recurrent convolutional network for image interpolation. Comprehensive experiments demonstrate that our method leads to a surprisingly superior performance and offers new state-of-the-art benchmark.