Gradient residual attention network for infrared image super-resolution

Image super-resolution techniques overcome the limitations of physical limits on infrared imaging systems and allow higher-resolution images of targets to be obtained on top of existing systems. In this paper, we proved that infrared images are more reconstructed than visible images based on data compression. We propose a CNN-based Gradient Residual Attention Network (GRAN) for infrared image super-resolution. Specifically, the residual dense module (RDB) is used to acquire depth features and the gradient operator (GO) gains fine-grained detail features. Meanwhile, the 3D attention block (3DAB) learns features' channel and spatial correlation to selectively capture more useful informative features. The experimental results show that the proposed method performs well compared to popular image super-resolution methods in terms of quantitative metrics and visual quality.