Super-Resolution of Satellite Images by two-Dimensional RRDB and Edge-Enhancement Generative Adversarial Network
Yu-Zhang Chen, Tsung-Jung Liu, Kuan-Hsien Liu
Abstract
With the increasing demand for high-resolution images, image super-resolution (SR) technology has become one of the focuses in related research fields. Generally speaking, high resolution is usually achieved by increasing the density and accuracy of the sensor. However, such an approach is quite expensive for equipment and design. In particular, increasing the density of satellite sensors must be undertaken great risks. Inspired by EEGAN and based on it, the Ultra-Dense Subnet (UDSN) and Edge Enhanced Network (EEN) were modified. Among them, the UDSN is used for feature extraction and obtains high-resolution results that look clear in the intermediate but are deteriorated by artifacts, and the Edge-Enhanced Subnet (EESN) is used to purify, extract and enhance the image contour and use mask processing to eliminate images contaminated by noise. Finally, the restored intermediate image and the enhanced edge are combined to produce a high-resolution image with high credibility and clear content. We use Kaggle and AID open experimental datasets to test and compare the results among different methods. It proves the performance of the proposed model is better than other SR methods.
BibTeX
@inproceedings{icassp2022_superresolutiono,
title = {Super-Resolution of Satellite Images by two-Dimensional RRDB and Edge-Enhancement Generative Adversarial Network},
author = {Yu-Zhang Chen and Tsung-Jung Liu and Kuan-Hsien Liu},
booktitle = {ICASSP 2022},
year = {2022}
}