Enhanced Attention-Based Back Projection Network for Image Super-Resolution in Sensor Network

In this article, we propose a novel method for image restoration in sensor network. Deep learning based image Super-Resolution (SR) is such process which takes low-resolution image as input and outputs high-resolution one by an end-to-end trainable architecture. It has shown rapid development in this field by constantly deepening and widening the SR model or combining with other specially designed structures. Recently non-local attention mechanism is proposed and introduced into SR field which focuses on the self-similarity of single feature or difference between channels, but those methods don’t use the mechanism to explore the relationship between features in different layers. In this article, we design a Back Projection Attention Module to learn the cross-relation between different types of image degradation and high-resolution components. Then we construct our network by stacking the iterative up-sampling and down-sampling process and concatenating all outputs of up-sampling process to finally improve the ability of characteristic expression. Extend experiments on four commonly used datasets show the proposed model can improve the performance in quantitative and qualitative measurements.