Edge-Enhanced GAN for Remote Sensing Image Superresolution

By Kui Jiang, Zhongyuan Wang, Peng Yi, Guangcheng Wang, T. Lu, Junjun Jiang. IEEE Transactions on Geoscience and Remote Sensing, 2019

485 citations recorded by the source corpus.

The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set, Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the-art SR approaches.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c5dae9d816a3a972e35ef0d55ddc3bfaa4d90361

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Siwei Yu
2. Qian Du
3. Qiangqiang Yuan
4. Jianwei Ma
5. Liangpei Zhang
6. José M. Bioucas-Dias
7. Xiaoqun Zhang

In Computer Vision and Pattern Recognition

1. Siwei Yu
2. Liangpei Zhang
3. Jianwei Ma
4. Qiangqiang Yuan
5. Qian Du
6. Xiaonan Luo
7. Long Sun
8. Nilanjan Ray

Citations in the corpus

• A review of machine learning in processing remote sensing data for mineral exploration (2021, methodology): “In more recent work, the weighted MD classifier based on relative offset boosted the accuracy (Wang and Jiang, 2019).”
• TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution (2024, methodology): “A. Remote Sensing Image Super-Resolution 1) CNN-Based: Drawing inspiration from SRCNN [32], a crowd of CNN-based SR methods have emerged and made remarkable progress on image SR task [33], [34], [35], [36], [37].”
• Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “To further improve the image resolution, the image contours were extracted with an edge-enhancement GAN to remove the artifacts and noise in super resolution (Jiang et al., 2019).”
• Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network (2020, methodology): “88.5% 81.1% ESRGAN + FRCNN SR - SR 93.6% 82% EEGAN + SSD SR - SR 88.1% 80.8% EEGAN + FRCNN SR - SR 93.1% 81.3% We also compared our results with different architectures. We used ESRGAN [21] and EEGAN [22] with the detectors for comparison. Table3clearly shows that our method delivers superior result”
• MADNet: A Fast and Lightweight Network for Single-Image Super Resolution (2020, background): “Addressing SISR has proven to be useful in many practical cases, such as video streaming [44], [50]; remote sensing [16], [58]; and medical imaging [31], [45], [48].”
• SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery (2022, background): “Recently, super resolution (SR) technology has shown great potential in remote sensing fields [15], [16], [17], [18].”