Fast and Efficient Edge Fusing Network Architectures for Accurate Single Image Super-resolution

Recovering a High-Resolution (HR) image from a Low-Resolution (LR) image is the main concept of image Super-Resolution (SR). Convolution Neural Networks (CNN) are becoming widely adopted in many applications including generation of HR images from LR images. Although CNNs are widely used with great performance improvements, there is still much room for improvement. There has always been a trade-off between the number of parameters and performance enhancement. This thesis presents a novel convolutional neural network architecture for high scale image SR inspired by the DenseNet and ResNet architecture. In particular, modifications can be made to the convolutional layers in the network: stacking the features and reusing the weight layers to increase the receptive field. It is shown how this method can be used to expand the receptive field and performance of super-resolution networks, without increasing the number of trainable parameters and sacrificing the computation time. These modifications can easily be integrated into any convolutional neural network to improve the accuracy by efficient high-level feature extraction while reducing training time and parameter numbers. Proposed methods are especially effective for the challenging high scale SR due to edge and texture recovery through the expanded network receptive field. Experimental results show that the proposed model outperforms the state-of-the-art methods.