Shallow and deep convolutional networks for image super-resolution
Ru Sen Fan, Sumei Li, Guoqing Lei, Guanghui Yue · 2017
A shallow and deep convolutional neural network is presented for the single-image super-resolution (SISR). The proposed method doesn't need hand-designed procedures, directly learning an end-to-end mapping between low-resolution (LR) and high-resolution (HR) images. The upsampling of the network by deconvolution leads to much more efficient and effective training, reducing the computational complexity of the overall SR operation. However, most existing methods based on CNNs for super resolution need preprocessing like bicubic interpolating LR images to the size of HR images. This method can restore more details by multi-scale manner, and has strong adaptability whether on images or videos. Our model is evaluated on different datasets, outperforming the existing methods in accuracy and visual impression.