Reconstructed Densenets for Image Super-Resolution

Lingfeng Wang, Linwei Qiu, Wei Sui, Chunhong Pan

Abstract: Deep learning has been successfully applied to single image super-resolution problem due to its high data fitting ability. However, the trending of deeper layers and wider receptive field to acquire better performance brings high computation complexity and serious information vanishing. To address this problem, we proposed a new Reconstructed DenseNets model for super-resolution. The basic idea behind Reconstructed DenseNets is to improve the recent DenseNets model by modifying the two core modules, dense blocks and transition blocks, so that the Reconstructed DenseNets can emphasize the quality of data reconstruction. Specifically, on the one hand, the batch normalization layers in dense blocks is ignored to overcome the data shift risk. One the other hand, the pooling layers in transition blocks is also ignored to ensure the ability to reconstruct. Based on the above two improvements, the new DenseNets is named as Reconstructed DenseNets. Extensive experiments evaluate the effectiveness of our model, showing the outperforming of the state-of-the-art approaches.