Dual Path Attention and Re-parameterization Network for efficient image super-resolution

Efficient deep learning based methods have achieved significant performance in single image super-resolution. Recent research on efficient super-resolution has mainly focused on reducing the number of parameters and computational complexity through various network designs, enabling models to be better deployed on resource constrained devices. In this work, we propose a novel and effective super-resolution model based on attention mechanism and structural re-parameterization, called Dual Path Attention and Re-parameterization Network DPARN , which uses a hybrid attention mechanisms to balance the running speed and reconstruction quality of the model. Specifically, we utilize grouped convolution to introduce both parameter free and enhanced spatial attention to improve the feature extraction capability of student networks. Meanwhile, we ado-pted a novel lightweight network training strategy that first uses knowledge distillation for initial training, during which structured knowledge from the teacher network is transmitted to the student network. Then, multiple loss functions are combined to fine-tuning the student network, in order to preserve high-frequency details and avoid excessive smoothing caused by pixel loss. Finally, extensive experiments conducted on four benchmark datasets demonstrated the effectiveness and efficiency of proposed DPARN. Our method achieves PSNR SSIM performance comparable to state-of-the-art efficient super-resolution models, with faster inference speed and fewer network parameters.