Involution-based network with contrastive learning for efficient image super-resolution

Single-image super-resolution (SISR) studies have achieved superior improvement with the development of convolution neural networks. However, most methods sink into the high computation cost. To tackle this issue, we propose an involution-based lightweight method with contrastive learning for efficient SISR. Unlike the original involution, we set the group number of involution operations to the input feature channels. This setting guarantees the spatial- and channel-specific peculiarity. Moreover, our implemented involution not only learns the weight but also the bias for convolution. Simultaneously, we rethink the kernel generation functions of involution. Instead, we utilize Sigmoid with reparameterized convolution. We additionally apply residual path to involution operation. Furthermore, contrastive learning is adopted during training to learn universal features. Compared with state-of-the-art efficient SISR methods, our proposed methods achieve the best performance with similar or fewer parameters.