Large receptive field convolutional neural network for image super-resolution
Qiang Wang, Huijie Fan, Yang Cong, Yandong Tang · 2017
This paper presents a new approach to Single Image Super Resolution (SISR), based upon Convolutional Neural Network (CNN). Although the SISR is ill-posed which can be seen as finding a non-linear mapping from a low to high-dimensional space. Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration and non-linear mapping problems. We consider the single image Super-Resolution (SR) problem as convolution operators and develop a CNN to capture the characteristics of Low-Resolution (LR) input image. We find that increasing the receptive field shows the improvement in accuracy. Our solution is to establish the connection between traditional optimization-based schemes and neural network architectures. In the paper a novel, separable structure is introduced as a reliable support for robust convolution against artifacts. Our proposed method performs better than existing methods in terms of accuracy and visual improvements in our results are easily noticeable.