Abstract
In recent years, super-resolution reconstruction has been introduced into DEM. The process of mapping low-resolution DEM images to high-resolution DEM is highly uncertain. At present, DEM super-resolution reconstruction methods mainly solve the problem by designing a more sophisticated network. However, the existing methods fail to capture the complex conditional distribution of high-resolution DEM during training, resulting in blurring and artifacts in the reconstruction results. Based on the lack of explicit, high-resolution DEM conditional distribution modeling, this paper proposes a reversible network model based on normalized flow. The model uses the characteristics of real low-resolution DEM images as conditions and learns to map the distribution of high-resolution DEM images to simple Gaussian distribution, thereby simulating the conditional distribution of high-resolution DEM. The negative log-likelihood function and pixel loss function are used to accelerate the optimization to generate high-resolution DEM images that are closer to the natural terrain. Experiments show that the proposed model can preserve the terrain features and achieve good performance. Especially on the test set, compared with the traditional interpolation method (Bicubic) and the existing deep learning methods (SRGAN and Internal-External), the PSNR results of this model are improved by 2.03%, 0.43%, and 2.58%, respectively.