Accurate Image Super-Resolution Using Cascaded Multi-Column Convolutional Neural Networks
Shuai Yuan, Yongfang Wang, Peng Ye, Yumeng Xia · 2018
Image super-resolution (SR) can be seen as finding a nonlinear mapping between low-resolution (LR) image and high-resolution (HR) image. Convolutional neural networks (CNN) based image SR methods have achieved great success based on features extracted from pretrained networks. However, the previous works less consider multiscale features. In this paper, we propose a SR cascaded multi-column convolutional neural network (SRCMCN) to recover HR image by learning multi-scale features from the corresponding LR image. Compared to existing CNN based image SR methods, the proposed method directly takes the original LR image as input to learn the residual between interpolated LR image and corresponding HR image, which will reduce the computational cost of convolution due to the small input size. Besides, in order to achieve high-quality reconstruction, we optimize our networks using mean absolute error (MAE) loss function. Experimental results demonstrate that our proposed method outperforms several state-of-the-art image SR methods in terms of both reconstructed image quality and computational efficiency.