One-to-One Mapping-Like Properties of DCN-Based Super-Resolution and its Applicability to Real-World Images

Although super-resolution techniques based on deep neural networks (SRDNN) have drawn significant interest and numerous algorithms have been proposed, they still have reliability problems and produce artefacts when applied to new datasets. In this paper, the working mechanisms of SRDNN techniques are analyzed in terms of data mapping. Since most SRDNN techniques can be viewed as dynamic linear projections, we analyzed a large number of projection vectors (over 70 million) and found that the SRDNN method performs one-to-one mapping-like operations and may be vulnerable to unknown data patterns. Then, we applied several SRDNN techniques to real-world images and analyzed the output images. The current SRDNN methods failed to distinguish the blurred edges/lines due to low resolutions from coding artefacts and enhanced both, even though the SRDNN methods were trained using compressed low-resolution (LR) images. These analyses and results indicate that current SRDNN methods may not be able to provide robust performance and new structures may be necessary for reliable super-resolution performance.