A GPU-accelerated DEM super-resolution method

Abstract

The invention discloses a DEM super-resolution method based on GPU acceleration. Including: (1) using the interpolation method to expand the low-resolution DEM learning data by K times, so that it reaches the same scale as the high-resolution DEM learning data; Resolution DEM reconstruction data; (2) divide the high-resolution DEM learning data, low-resolution DEM learning data and low-resolution DEM reconstruction data into a series of overlapping regional blocks of size N×N; (3) For each area block of the low-resolution DEM reconstruction data, search for similar blocks in the low-resolution DEM learning data, calculate the similar weight of the area block, and compare the similar weight with a given threshold T to obtain the high-resolution The DEM region block. The invention can efficiently and quickly reconstruct high-resolution DEM data, and the reconstruction result is clear and accurate.

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

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