A Convolutional Neural Network for Image Super-Resolution Using Internal Dataset

Deep convolutional neural networks have recently achieved dramatic success in super-resolution (SR) performance in the past few years. However, the parameters of the mapping functions of these networks require an external dataset for training. In this paper, we propose a convolutional network for image super-resolution reconstruction that can be trained using an internal dataset constructed using a single image. The proposed single image convolutional neural network (SICNN) is designed with two branches. First, a large scale-feature branch trains the feature mappings that are from the low resolution (LR) image patches to the high- resolution image (HR) patches. The LR image patches are the enlarged image patches via bicubic interpolation. Second, the small scale-feature branch trains the feature mappings that are from the down-sampling image patches to the enlarged image patches. In contrast to the existing SR networks, the SICNN enjoys two desirable properties: 1) it does not require external datasets to conduct training, and 2) it enlarges an SR image at an arbitrary scale while restoring the clear edges and textures. The results of evaluations on a wide variety of images show that the proposed SICNN achieves advantages over the state-of-the-art methods in terms of both numerical results and visual quality.