A Low- and High-Resolution Terahertz Image Pair Construction Method With Gradient Fusion for Learning-Based Super-Resolution

Abstract

Quality and feature quantity of low- and high-resolution image pairs for training are directly related to performance of learning-based super-resolution methods. In order to accurately increase learning features to the low- and high-resolution image pairs, we proposed a gradient degradation model of terahertz(THz) images to describe the process of a high-resolution THz image’s gradient map is how degraded to corresponding low-resolution THz image’s gradient map. And with the proposed model, we presented a low- and high-resolution THz image pair construction method with gradient fusion for learning-based super-resolution. As gradient maps are fused to training pairs, performance of learning-based super-resolution methods for THz images could be improved. In addition, we applied the low- and high-resolution THz image pair construction method with gradient fusion to very deep super-resolution(VDSR) method and zero shot super-resolution(ZSSR) method, named VDSR method with gradient fusion and ZSSR method with gradient fusion, respectively. To evaluate the performance of these two improved methods, comparison experiments with passive millimeter-wave images and our THz lab data are presented. The experimental results show that the performance of the VDSR method with gradient fusion and the ZSSR method with gradient fusion have significant improvements in peak signal-to-noise ratio and structural similarity index measure than the VDSR method and the ZSSR method, respectively. It indicates that performance of learning-based super-resolution methods for THz images could be improved by applying the proposed low- and high-resolution THz image pair construction method with gradient fusion.

Keywords

• Gradient map
• neural network
• super-resolution
• terahertz image