Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution
This work addresses the problem of improving image super-resolution quality for applications like photography and medical imaging, but it is incremental as it builds on existing deep learning approaches.
The paper tackles the limitation of fully convolutional networks in single image super-resolution by proposing a new architecture that uses a fully connected layer for upsampling to exploit global contextual information, and it outperforms existing state-of-the-art methods in experiments.
Recently, deep neural networks have achieved impressive performance in terms of both reconstruction accuracy and efficiency for single image super-resolution (SISR). However, the network model of these methods is a fully convolutional neural network, which is limit to exploit the differentiated contextual information over the global region of the input image because of the weight sharing in convolution height and width extent. In this paper, we discuss a new SISR architecture where features are extracted in the low-resolution (LR) space, and then we use a fully connected layer which learns an array of differentiated upsampling weights to reconstruct the desired high-resolution (HR) image from the final obtained LR features. By doing so, we effectively exploit the differentiated contextual information over the whole input image region, whilst maintaining the low computational complexity for the overall SR operations. In addition, we introduce an edge difference constraint into our loss function to preserve edges and texture structures. Extensive experiments validate that our SISR method outperforms the existing state-of-the-art methods.