内容适应式展开波形变压器用于超光谱图像超分辨率
概括
我们介绍了一种内容适应式展开波纹变压器 (CAUWT),用于高光谱图像超分辨率 (HSI-SR). 这种方法提高了适应性和高频细节捕获,以较低的计算成本优于现有技术.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 超分辨率的高光谱图像 (HSI-SR) 通常将高分辨率的多光谱图像 (HR-MSI) 与低分辨率的高光谱图像 (LR-HSI) 融合在一起.
- 深度展开框架提供了一个结构化的方法,但在数据适应性和高频信息捕获方面存在局限性.
- 现有的HSI-SR方法在固定的参数和不充分的变压器能力上扎,以获得详细的光谱空间信息.
研究的目的:
- 通过提出一个新的深度展开框架来解决当前HSI-SR方法的局限性.
- 增强数据模块的适应性,并改进先前模块中的高频信息提取.
- 为了实现优越的HSI-SR性能,减少计算开销.
主要方法:
- 拟议的内容适应式展开波形变压器 (CAUWT) 具有代适应式参数学习.
- 推出了波形辅助变压器 (WAT),集成离散波形变压器 (DWT) 和混合光谱空间注意力区块 (HSSAB).
- DWT捕捉了多个尺度,多个频率的细节;HSSAB模型在光谱空间子频段内的相关性.
主要成果:
- 在模拟和现实数据集上,CAUWT在HSI-SR中表现出显著的改进.
- 拟议的波纹辅助变压器有效地提高了高频信息质量,而无需增加网络复杂性.
- 实验结果显示,与主流的HSI-SR方法相比,性能优越.
结论:
- 拟议的CAUWT方法有效地解决了深度展开的HSI-SR中的关键问题.
- 适应性参数学习和新型WAT显著提高了HSI-SR的性能.
- CAUWT实现了最先进的结果,提高了效率.
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