Efficient sub-pixel convolutional neural network for terahertz image super-resolution

Abstract

Terahertz waves are electromagnetic waves located at 0.1-10 THz, and terahertz imaging technology can be applied to security inspection, biomedicine, non-destructive testing of materials, and other fields. At present, terahertz images have unclear data and rough edges. Therefore, improving the resolution of terahertz images is one of the current hot research topics. This paper proposes an efficient terahertz image super-resolution model, which is used to extract low-resolution (LR) image features and learn the mapping of LR images to high-resolution (HR) images, and then introduce an attention mechanism to let the network pay attention to more information features. Finally, we use sub-pixel convolution to learn a set of scaling filters to upgrade the final LR feature map to an HR output, which not only reduces the model complexity, but also improves the quality of the terahertz image. The resolution reaches 31.67 db on the peak signal-to-noise ratio (PSNR) index and 0.86 on the structural similarity (SSIM) index. Experiments show that the efficient sub-pixel convolutional neural network used in this article achieves better accuracy and visual improvement compared with other terahertz image super-resolution algorithms.