Recurrent Dual-level Residual Network for Image Super-resolution Reconstruction

Deep learning-based methods for image super-resolution reconstruction often have a large model scale and low efficiency. Therefore, we propose an image super-resolution reconstruction network that can balance performance and network size well. We design a dual-level residual feature extraction module by using the local wider residual block structure. Using this module as a basis, we construct a deep feature extraction network by the feature map cyclic transfer method, which allows multiple network layers to share parameters and improve the efficiency of the network. In addition, we construct an upsampling module using a multiscale joint learning mechanism, which improves the commonly used upsampling method to compensate for the loss of high-frequency information caused by resolution expansion. Experimental results show that compared with the same type of network, the proposed network achieves excellent performance indicators and visual effects while maintaining a small network size.