音色图像分类和加权学习用于可见和NIR图像融合
Chan-Gi Im1, Dong-Min Son1, Hyuk-Ju Kwon1
1School of Electronic and Electrical Engineering, Kyungpook National University, 80 Deahakro, Buk-Gu, Daegu 41566, Korea.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了一种使用DenseFuse,一个卷积神经网络 (CNN) 的快速图像融合方法,以加速可见和近红外 (NIR) 图像合成. 基于CNN的新方法显著提高了处理速度,同时保持了高图像质量.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 基于规则的可见和近红外 (NIR) 图像合成方法的处理速度较慢.
- 现有的基于学习的方法往往会损害图像质量或可见性.
研究的目的:
- 开发一种快速有效的图像融合方法,用于合成可见和近射线图像.
- 用卷积神经网络 (CNN) 提高传统基于规则的方法的处理速度.
主要方法:
- 提出了一种使用DenseFuse,一种基于CNN的架构的新型图像融合方法.
- 该方法包含用于数据集准备的光扫描算法和基于亮度和偏差的分类技术.
- 在特定的聚变层内研究特征图的合成,并与其他聚变层方法进行比较.
主要成果:
- 与现有的基于学习的方法相比,提出的DenseFuse方法实现了优越的图像质量和可见性.
- 合成的图像保留了基于规则的方法的高视觉质量特征.
- 与基于规则的图像合成方法相比,处理时间至少缩短了三倍.
结论:
- 基于DenseFuse的图像融合方法为可见和NIR图像合成提供了显著的处理速度改进.
- 这种方法成功地将增强的速度与高质量的图像合成相平衡,优于现有的基于学习的技术.
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