Single image super-resolution with multi-level feature fusion recursive network

Abstract

Recursive learning can widen the receptive field of deep convolutional neural network while do not increase model parameters with parameters sharing. And dense skip connections can promote deep feature representation ability by reusing deep features in different receptive fields. Both techniques are very beneficial to improve performance in image restoration tasks. In this paper, we propose a new end to end deep network for single image super-resolution (SISR) by using both recursive residual feature extraction and multi-level features fusion, in which the multi-level deep features are firstly produced from the input low resolution (LR) image by recursive convolution units, and then fused to reconstruct high resolution (HR) image. The proposed scheme could achieve good super-resolution performance with relatively low complexity. Extensive experimental results on the benchmark tests verify the effectiveness of the proposed method.