A Real-Time Super-Resolution Method Based on Convolutional Neural Networks

The aim of single-image super-resolution is to recover a high-resolution image based on a low-resolution image. Deep convolutional neural networks have largely enhanced the reconstruction performance of image super-resolution. Since the input image is always bicubic-interpolated, the main weakness of deep convolutional neural networks is that they are time-consuming. Moreover, fast convolutional neural networks can perform real-time image super-resolution but are unable to achieve reliable performance. To address those drawbacks, we propose a real-time image super-resolution method with good reconstruction performance. We replace the default upsampling method (bicubic interpolation) with a pixel shuffling layer. Local and global residual connections are taken to guarantee better performance. As shown in Fig. 1, our proposed method is not only fast but also accurate.