Asymmetric convolutional modulation network for efficient image super-resolution

Thanks to the effectiveness of large kernel convolutions in acquiring large receptive fields, lightweight single image super-resolution (SISR) approaches based on large kernel designs have achieved significant performance. However, these state-of-the-art methods still require high computational costs, and their model efficiency remains to be improved. To address these challenges, we propose an asymmetric convolutional modulation network (ACMN) for efficient image super-resolution. In detail, we design an asymmetric convolutional modulation unit (ACMU) that combines large kernel design and modulation mechanism for adaptively selecting representative features from a large receptive field. Furthermore, we adopt depth-wise asymmetric convolutions to further reduce computational burden. To supplement the local context information and promote inter- channel interaction, we further develop a channel shuffle feedforward network (CSFN) to extract local feature information and facilitate inter-channel information interaction. Experimental results demonstrate that our ACMN outperforms other state-of-the-art efficient SR methods with fewer parameters and Multi-Adds.