Remote Sensing Image Dehazing Using Multi-Scale Gated Attention for Flight Simulator
Year: 2024
Type: article
Abstract: For flight simulators, it is crucial to create three-dimensional terrain using clear remote sensing images. However, due to haze and other contributing variables, the obtained remote sensing images typically have low contrast and blurry features. In order to build a flight simulator visual system, we propose a deep learning-based dehaze model for remote sensing images dehazing. An encoder-decoder architecture is proposed that consists of a multiscale fusion module and a gated large kernel convolutional attention module. This architecture can fuse multi-resolution global and local semantic features and can adaptively extract image features under complex terrain. The experimental results demonstrate that, with good generality and application, the model outperforms existing comparison techniques and achieves high-confidence dehazing in remote sensing images with a variety of haze concentrations, multi-complex terrains, and multi-spatial resolutions.
Authors: Qi LIU, Bo WANG, Shihan TAN, Shurong ZOU, Wenyi GE
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402084040