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Updated: Jun 19, 2026
14:25
Determining 3D Flow Fields via Multi-camera Light Field Imaging
Published on: March 6, 2013
A DEM super resolution reconstruction method based on normalizing flow
Jie Yu1, Yangtenglong Li2,3,4, Xuan Bai5,6
1College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu, 610059, China.
Scientific Reports
|March 28, 2025
Summary
This study introduces a novel reversible network model for super-resolution reconstruction of digital elevation models (DEM). The method effectively simulates high-resolution DEM distribution, improving terrain feature preservation and reconstruction accuracy.
Area of Science:
- Geoinformatics
- Computer Vision
- Remote Sensing
Background:
- Super-resolution reconstruction for Digital Elevation Models (DEM) presents challenges in accurately mapping low-resolution to high-resolution data.
- Existing methods often struggle to model complex conditional distributions, leading to blurred results and artifacts.
Purpose of the Study:
- To propose a novel reversible network model based on normalized flow for enhanced DEM super-resolution reconstruction.
- To explicitly model the conditional distribution of high-resolution DEM using low-resolution image characteristics.
Main Methods:
- Developed a reversible network utilizing normalized flow to map high-resolution DEM distributions to a Gaussian distribution.
- Employed negative log-likelihood and pixel loss functions to optimize the model.
- Conditioned the model on real low-resolution DEM image characteristics.
Main Results:
- The proposed model successfully preserves terrain features and generates high-resolution DEMs closer to natural terrain.
- Achieved improved Peak Signal-to-Noise Ratio (PSNR) compared to Bicubic, SRGAN, and Internal-External methods by 2.03%, 0.43%, and 2.58%, respectively.
Conclusions:
- The normalized flow-based reversible network offers a robust solution for DEM super-resolution.
- The explicit modeling of conditional distributions significantly enhances reconstruction quality and accuracy.
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