DEM super-resolution method based on GPU acceleration

Abstract

The present invention discloses a DEM super-resolution method based on GPU acceleration The DEM super-resolution method comprises: (1) expanding low-resolution DEM learning data for K times by utilizing an interpolation method so as to enable the low-resolution DEM learning data to reach the same scale with high-resolution DEM learning data, and simultaneously expanding reconstructed DEM data for K times by the same interpolation method to obtain low-resolution DEM reconstructed data; (2) respectively partitioning the high-resolution DEM learning data, the low-resolution DEM learning data and the low-resolution DEM reconstructed data into a series of mutually overlapped domain blocks with the size of N*N; and (3) carrying out similar block search on each domain block of the low-resolution DEM reconstructed data in the low-resolution DEM learning data, calculating a similar weight of the domain blocks and by comparing the similar weight with a given threshold value T, obtaining high-resolution DEM domain blocks. According to the present invention, the high-resolution DEM data can be efficiently and rapidly reconstructed; the reconstruction result is clear; and accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of terrain surveying and mapping, and more particularly relates to a DEM super-resolution method based on GPU acceleration. Background technique [0002] Digital elevation model (Digital Elevation Model, DEM) is a branch of digital terrain model, which is a digital model that expresses ground elevation in the form of a set of ordered numerical arrays. With the high development of digitalization, DEM models have extremely high application value in social life, so people's requirements for high-precision terrain models are also getting higher and higher. In order to obtain a high-precision DEM model, two methods are usually used. One method is to directly improve the accuracy of the DEM model by using more advanced hardware equipment. This method is not only costly, but also requires a relatively high level of technology; the second method is to analyze the DEM model and use the super-resolution method To im...

AI Technical Summary

Problems solved by technology

Learning-based super-resolution is a hot topic in current super-resolution research. It focuses more on understanding the nature and internal laws of high-resolution images, so it has better reconstruction results. However, this method requires a large learning database. The amount of calculation is very huge, so it is of practical significance to study fast super-resolution methods

Images