Progressive Interaction-Learning Network for Lightweight Single-Image Super-Resolution in Industrial Applications

Recently, deep learning (DL)-based industrial applications have attracted broad attention due to their advanced performance. However, the limited computational resource in portable devices always makes big DL models inapplicable in the industry. DL-based single-image super-resolution also encounters this problem because of its large computations. Besides, most lightweight convolutional-neural-network-based methods utilize features insufficiently, which restricts their capability for industrial reconstruction. To alleviate this problem, we present a progress interaction-learning network (PILN) to refine features at different levels: at the global level, we employ a progressive interaction-learning strategy to integrate hierarchical features in temporal and spatial dimensions; at the mediate level, enhanced interaction-learning units, adopting the enhanced interactive study, significantly boost the reconstruction performance; at the local level, employing pixelwise learning, residual cells are raised to search for an optimal information flow by weight distribution. Extensive experiments demonstrate that the PILN outperforms other state-of-the-art methods.