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pro vyhledávání: '"Yuan, Maoxun"'
Infrared and visible image fusion aims to utilize the complementary information from two modalities to generate fused images with prominent targets and rich texture details. Most existing algorithms only perform pixel-level or feature-level fusion fr
Externí odkaz:
http://arxiv.org/abs/2410.22837
Semantic analysis on visible (RGB) and infrared (IR) images has gained attention for its ability to be more accurate and robust under low-illumination and complex weather conditions. Due to the lack of pre-trained foundation models on the large-scale
Externí odkaz:
http://arxiv.org/abs/2404.17360
In recent years, object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention and has been widely implemented across a diverse array of fields. By leveraging the complementary properties between RGB
Externí odkaz:
http://arxiv.org/abs/2401.10731
Pan-sharpening, as one of the most commonly used techniques in remote sensing systems, aims to inject spatial details from panchromatic images into multispectral images (MS) to obtain high-resolution multispectral images. Since deep learning has rece
Externí odkaz:
http://arxiv.org/abs/2306.16181
Autor:
Yuan, Maoxun, Wei, Xingxing
Object detection on visible (RGB) and infrared (IR) images, as an emerging solution to facilitate robust detection for around-the-clock applications, has received extensive attention in recent years. With the help of IR images, object detectors have
Externí odkaz:
http://arxiv.org/abs/2306.16175
Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) images can provide complementary information to handle light variations, t
Externí odkaz:
http://arxiv.org/abs/2209.13801
Publikováno v:
In Information Fusion May 2024 105
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