Deep Learning Based Super Resolution Using Significant and General Regions

Today, Big Data brings benefits to many areas of scientific research. However, processing these large amounts of data often requires extensive computing time and a large storage space. Global feature analysis is considered to be universal in traditional super resolution methods, but it is not applicable to Big Data. There remains a viewpoint that it is not necessary to address all data equally and impartially. Focusing on useful information can make the massive data analysis possible and more effective. In this paper, we consider the significant regions, and thus, we propose a new super resolution approach that uses significant and general information. Under the framework of a convolutional neural network, the training process is performed on the significant parts of the training data set, and the reconstruction process considers significant parts separately; then, a super resolution image will be obtained according to each different demand. This concept is easy to understand, but it can be achieved only via the Big Data approach with many similar images on the Internet and the effective deep learning algorithm. Experiments show that our new approach can reduce the testing time and obtain a high-quality reconstructed image.