An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution
Chulalongkorn University（朱拉隆功大学）
Naveed Sultan, Amir Hajian, Supavadee Aramvith
备注：
Preprint of paper from The 21st International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology or ECTI-CON 2024, Khon Kaen, Thailand
In recent years, convolutional neural networks (CNNs) have achieved remarkable advancement in the field of remote sensing image super-resolution due to the complexity and variability of textures and structures in remote sensing images (RSIs), which often repeat in the same images but differ across others. Current deep learning-based super-resolution models focus less on high-frequency features, which leads to suboptimal performance in capturing contours, textures, and spatial information. State-of-the-art CNN-based methods now focus on the feature extraction of RSIs using attention mechanisms. However, these methods are still incapable of effectively identifying and utilizing key content attention signals in RSIs. To solve this problem, we proposed an advanced feature extraction module called Channel and Spatial Attention Feature Extraction (CSA-FE) for effectively extracting the features by using the channel and spatial attention incorporated with the standard vision transformer (ViT). The proposed method trained over the UCMerced dataset on scales 2, 3, and 4. The experimental results show that our proposed method helps the model focus on the specific channels and spatial locations containing high-frequency information so that the model can focus on relevant features and suppress irrelevant ones, which enhances the quality of super-resolved images. Our model achieved superior performance compared to various existing models.
A Signal Extraction Approach for Remote Heart Rate Variability Assessment Using Proxy Measure in a Driving Simulator
University of Belgrade - School of Electrical Engineering（贝尔格莱德大学电气工程学院）；Vinča Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade（温查核科学研究所-塞尔维亚共和国国家研究所，贝尔格莱德大学）；Faculty of Electrical Engineering, University of Ljubljana（卢布尔雅那大学电气工程学院）
