Progressive Feature Enhancement Network for Image Super-Resolution Reconstruction

In order to achieve a better balance between image restoration quality and network parameters, this paper proposes a super-resolution (SR) method based on progressive feature enhancement network. The method mainly consists of two modules: shallow information enhancement module and deep information enhancement module. In the shallow information enhancement module, firstly, the shallow information of low-resolution (LR) image is extracted by single convolution layer, and then we design the multi-scale attention block to achieve feature extraction and enhancement. The deep information enhancement module first uses the residual learning block to learn the deep information of the image and then employs the designed multi-scale attention block to obtain the enhanced deep multi-scale information. Finally, we use a skip-connection to fuse the shallow information obtained at the first layer and the deep multi-scale information at the pixel level and then up-sample the fusion feature maps to obtain the final high-resolution (SR) image. The experimental results show that, compared with some mainstream deep learning SR methods, the image reconstructed by the proposed method has achieved better results in both subjective and objective indicators.