Lightweight Progressive Residual Clique Network for Image Super-Resolution

Deeper and wider convolutional neural networks (CNN) hava been widely applied to the single image super-resolution (SR) task for its appealing performance. However, enormous parametric memory footprint hinders its real-time application on mobile devices, especially in the energy-sensitive environment. In this work, we take both the reconstruction performance and efficiency into consideration and propose a lightweight progressive residual clique network (PRCN) for image SR. PRCN is built on the two-stage residual channel separation block (RCSB) and long-skip connections. First, we divide the input into four channel groups to differently learn texture details, immediately followed by a primary fusion to establish cross-channel correspondence in the first stage. Then we perform a further fusion on the outputs of the first stage to constitute a clique for the refinement in the second stage. Meanwhile, we employ SENet to improve the outputs of the second stage with the separate features of the first stage. This design not only enforces the correlation across channels, but also allows fewer densely connected blocks. Experimental results on public datasets show that PRCN outperforms state-of-the-art methods in terms of performance and complexity.