Single Image Super-Resolution Using Deep CNN with Dense Skip Connections and Inception-ResNet

Recent studies have shown that the use of deep convolutional neural networks (Deep CNNs) can significantly improve the performance of single-image super-resolution reconstruction. In this paper, we propose a highly accurate and fast single-image super-resolution reconstruction (SISR) method by introducing dense skip connections and Inception-ResNet in deep convolutional neural networks. In the proposed network, all previous layer feature maps are used as input for each subsequent layer to promote feature reuse and alleviate the vanishing-gradient problem. In addition, the parallelized CNNs structure used reduces the size of the output of the previous layer, thereby accelerating the calculation speed and reducing the feature loss. Moreover, we only learn the residuals between the high-resolution images and the low-resolution images, and use the adjustable gradient cropping to achieve a very high learning rate. Results on benchmark datasets demonstrate that the proposed model not only achieves higher accuracy but also enables faster and more efficient calculations than the state-of-the-art methods.