长波红外分离焦点平面极化图像的联合排斥-demosicking网络与混合噪声水平估计
概括
我们开发了一种新的深度学习方法,同时消除和删除长波红外 (LWIR) 极化图像. 这种方法通过避免连续处理错误来提高图像质量,增强视觉应用.
科学领域:
- 光学和光子学 在光学和光子学.
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 剥离和剥离对于处理长波红外 (LWIR) 分离焦平面 (DoFP) 极化图像至关重要.
- 目前的方法连续应用denoising和demosaicking,导致累积错误和性能降低.
研究的目的:
- 为LWIR DoFP图像提出一个联合的denoising和demosaicking方法.
- 增强深度学习模型的泛化能力,用于极化图像恢复.
主要方法:
- 一个三阶段的渐进的深卷积神经网络被开发用于联合denoising和demosaicking.
- 模拟了混合的Poisson-Additive-Stripe噪声,并使用最小平方问题来估计来自真实LWIRDoFP图像的噪声参数.
- 估计的噪声参数被用来为神经网络生成合成训练数据.
主要成果:
- 拟议的方法有效地执行LWIR DoFP极化图像的联合无色化和无色化.
- 噪音建模和数据生成策略改善了网络对现实数据的概括能力.
- 实验结果证实,与现有的顺序方法相比,图像恢复性能优越.
结论:
- 联合深度学习方法在处理LWIR DoFP极化图像方面取得了重大进展.
- 精确的噪音建模和数据增强对于基于深度学习的强大图像恢复至关重要.
- 开发的方法增强了LWIR极化成像在各种应用中的实用性.
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