Super-resolution simplification network based on densely connected structure

In recent years, with the development of deep neural networks(DNNs) and their application in the field of super-resolution, the effect of image super-resolution reconstruction has been significantly improved.However, the pervious works mainly focus on good performance of model, ignoring enormous parameters and huge number of computations, which seriously restricts the practical application range of deep learning methods in image super-resolution reconstruction. Aiming at this issue, we designed a novel network based on Dense Net, and our work mainly lied in 3 aspects for improvement: 1) proposing a new architecture based on densely connected structure; 2) adding 1×1 convolutional layers as a feature selector to reduce the computations; 3) exploring the relationship among the number of channels, the reconstruction precision and the calculation amount. Experiment's results indicate that our model get comparable reconstruction precision results with other convolutional neural networks model, and our model takes only half of super-resolution time compared with fast super resolution convolutional neural network(FSRCNN).