Progressive back-projection networks for large-scale super-resolution

Recently, experiments show that deep super-resolution (SR) networks with error feedback mechanisms can achieve better accuracies than purely feed-forward networks. However, such error feed-back networks use one-step mapping, which increases the difficulty for training with large-scale factors. We first propose a stage-progressive back-projection network to progressively reconstruct images by simply dividing the entire back-projection stage into several levels. We convert one-step large-scale sampling into multiple samplings with a moderate scale factor through such division. At each level, we process low-resolution-to-high-resolution/ high-resolution-to-low-resolution mapping with a scale factor of 2. In order to enhance the effect of dense connections and to further improve the performance, we propose the unit-progressive back-projection network, in which we construct progressive projection units to avoid one-step mappings with large-scale factors. Additionally, we recommend a subpixel convolutional layer and its inverse transform as the mapping layer since it computes each pixel of the outputs covering more pixels from the inputs. Extensive quantitative and qualitative evaluations on benchmark datasets show that our algorithms substantially improve performance on large-scale SR tasks. (C) 2019 SPIE and IS&T