红外图像超分辨率网络使用增强型变压器和U-Net.
Feng Huang1, Yunxiang Li1, Xiaojing Ye1
1School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究介绍了SwinAIR-GAN,这是一种用于红外图像超分辨率 (SR) 的新型深度学习方法. 它提高了红外图像质量和细节重建,解决了硬件成本限制.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 红外成像技术 红外成像技术
背景情况:
- 红外图像对于遥感和消防安全至关重要,但受到高硬件成本的限制.
- 基于深度学习的超分辨率 (SR) 已经推进了图像重建,但红外图像SR仍未得到充分探索.
研究的目的:
- 为红外图像超分辨率 (SR) 开发一个有效的深度学习模型.
- 为了应对特征提取,融合和实红外图像中文物减少的挑战,SR.
主要方法:
- 设计了用于特征提取和融合的剩余变压器和平均聚合区 (RSTAB).
- 建议SwinAIR用于高级红外图像SR重建.
- 通过将SwinAIR与U-Net集成,开发了SwinAIR-GAN,结合了光谱正常化,脱落和文物歧视损失,以模拟真实红外图像退化.
主要成果:
- 斯温艾尔有效地提取和融合各种频率特征,以获得卓越的SR性能.
- SwinAIR-GAN显著改善了真实红外图像的SR重建.
- 该方法成功地重建了现实的纹理和细节,通过定性和定量评估进行验证.
结论:
- 拟议的SwinAIR-GAN方法为红外图像超分辨率提供了有效的解决方案.
- 这种方法克服了高硬件成本的局限性,通过先进的深度学习技术提高图像质量.
- 该方法在红外图像中重建现实的细节方面表现出强大的性能.
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