Deep skip connection and multi-deconvolution network for single image super-resolution

Abstract

In this paper, we propose an efficient single image super-resolution (SR) method for multi-scale image texture recovery, based on Deep Skip Connection and Multi-Deconvolution Network. Our proposed method focuses on enhancing the expression capability of the convolutional neural network, so as to significantly improve the accuracy of the reconstructed higher-resolution texture details in images. The use of deep skip connection (DSC) can make full use of low-level information with the rich deep features. The multi-deconvolution layers (MDL) introduced can decrease the feature dimension, so this can reduce the computation required, caused by deepening the number of layers. All these features can reconstruct high-quality SR images. Experiment results show that our proposed method achieves state-of-the- art performance.