Enhanced local distribution learning for real image super-resolution

Abstract

Previous work has shown that CNN-based local distribution learning can efficiently reconstruct high-resolution images, but with limited performance improvement against complex degraded images. In this paper, we propose an enhanced local distribution learning framework, called ELDRN, which successfully generalizes local distribution learning to realistic images whose degradation process is complex and unknowable. The cores of our ELDRN are the parallel attention block and dilated neighborhood sampling. The former mines discriminative features at both spatial and channel levels, that is, parameters for constructing local distributions, thus improving the robustness of distributions to real degradation patterns. To deal with the fact that the reference range of the target sub-pixel is not exactly equal to its neighborhood, we explicitly increase the sampling density, i.e., fusing more sampled pixels to produce the target sub-pixel. Experiments conducted on RealSR dataset illustrate that our ELDRN outperforms recent learning-based SISR methods and reconstructs visually-pleasant high-quality images.