Lightweight image super-resolution network using involution

Recently, the single image super-resolution methods with deep and complex convolutional neural network structures have achieved remarkable performance. However, those approaches improve the performance at the cost of higher memory occupation, which are difficult to be applied for some resource-constrained devices. With the goal of minimizing parameters, an effective and efficient operator named involution is introduced in our proposed model, delivering enhanced performance at reduced cost compared to convolution-based counterparts. On the basis of involution, we propose two building blocks named RMFDB(Residual Mixed Feature Distillation Block) and CICB(Conv-Invo-Conv Block) for the main module and the reconstruction module respectively. RMFDB has the similar structure as the RFDB but with our involution layers. This block is much more lightweight and efficient than conventional convolution-based blocks. CICB combines the nearest-neighbor upsampling, convolution and involution layers. The final reconstruction quality is improved with little parameter cost. Experimental results demonstrate the effectiveness of the proposed model against the state-of-the-art (SOTA) SR methods. Our final model could achieve similar performance as the lightweight networks RFDN and PAN, but with only 224K parameters and 64.2G Multi-Adds with the scale factor of 2. The effectiveness of each proposed components is also validated by ablation study.