Single Image Super Resolution via a Refined Densely Connected Inception Network
Tao Jiang, Yu Zhang, Xiaojun Wu, Gang Lü, Fei Hao, Yumei Zhang · 2018
Single image super resolution has achieved a significant breakthrough with the development of deep learning technology. Among these approaches based on deep learning, the mainstream method is to build a cascading network and attempt to add more learning layers. However, as the depth of the model increases, features far away from the reconstruction layer are less considered in the reconstruction process. In this paper, we propose a novel model based on a refined densely connected network for super-resolution reconstruction tasks. By utilizing densely connected paths in the model, we can significantly shorten the distance between the feature maps from different levels and the reconstruction layer. Besides, an inception-like structure is employed to replace the ordinary convolutional layer to take full advantage of the contextual information. Moreover, quantities of 1 ×1 filters are used to ensure an acceptable model size. Extensive experiments are conducted for demonstrating that the proposed method can achieve the state-of-the-art performance with smaller model size.