Multi-Scale Invertible Network for Image Super-Resolution.

Deep convolutional neural networks (CNNs) based image super-resolution approaches have reached significant success in recent years. However, due to the information-discarded nature of CNN, they inevitably suffer from information loss during the feature embedding process, in which extracted intermediate features cannot effectively represent or reconstruct the input. As a result, the super-resolved image will have large deviations in image structure with its low-resolution version, leading to inaccurate representations in some local details. In this study, we address this problem by designing an end-to-end invertible architecture that can reversely represent low-resolution images in any feature embedding level. Specifically, we propose a novel image super-resolution method, named multi-scale invertible network (MSIN) to keep information lossless and introduce multi-scale learning in a unified framework. In MSIN, a novel multi-scale invertible stack is proposed, which adopts four parallel branches to respectively capture features with different scales and keeps balanced information-interaction by branch shifting. In addition, we employee global and hierarchical feature fusion to learn elaborate and comprehensive feature representations, in order to further benefit the quality of final image reconstruction. We show the reversibility of the proposed MSIN, and extensive experiments conducted on benchmark datasets demonstrate the state-of-the-art performance of our method.