MSANet: multi-scale asymmetric network for efficient image super-resolution

Existing efficient convolutional attention-based super-resolution methods are limited by a constrained receptive field, which results in inadequate detail reconstruction and poor high-frequency feature extraction. To address this issue, we propose a multi-scale asymmetric network, called MSANet, which integrates several strategies to enhance performance and overcome the receptive field limitation. First, we introduce a multi-scale convolutional attention module (MACA), by combining large-kernel attention, multi-scale strategies, and asymmetric structures. Large-kernel attention expands the information aggregation range, thus significantly increasing the receptive field. The multi-scale strategy enhances the effective receptive field by extracting features at multiple levels, thereby improving detail representation. The asymmetric structure allows flexible kernel size configuration, which enables the model boosting the receptive field with minimal computational overhead. Second, to efficiently aggregate features from MACA, we design an enhanced spatial gated block (ESGB), which integrates a gating mechanism and spatial attention. This enables the network to adaptively regulate the information flow, thus improving image quality. Finally, we design a multi-scale asymmetric feature extraction block (MAEB) based on the efficient MetaFormer framework. This ensures both high performance and low resource consumption. Experimental results show that MSANet outperforms most existing efficient super-resolution methods, such as VSCNet, CTE-Net, and demonstrates strong generalization capabilities.