Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks.

Deep learning methods, in particular, trained convolutional neural networks (CNNs) have recently been shown to produce compelling results for single image super-resolution (SR). Invariably, a CNN is learned to map the low resolution (LR) image to its corresponding high resolution (HR) version in the spatial domain. We propose a novel network structure for learning the SR mapping function in an ima...