Subspace Constraint for Single Image Super-Resolution.

Recently, single image super-resolution (SISR) algorithms based on convolutional neural networks (CNN) have proliferated and achieved significant success. However, most of them use the same constraint to both low-frequency and high-frequency features in the loss function. They do not discriminate between high-frequency details and low-frequency information, which limits the representation capacity of high-frequency information. This paper presents a subspace constraint approach for SISR to discriminate between high-frequency information and low-frequency information and enhance the reconstruction of high-frequency features. In our approach, the constraint is introduced in wavelet domain. Meanwhile, our approach adopts the multi-level residual learning to improve the training efficiency. Extensive experimental results on five benchmark datasets show that our model is superior to those state-of-the-art methods for both accuracy and visual comparisons.