基于物理启发的深度网络的热图像处理
Thermal Image Processing via Physics-Inspired Deep Networks
摘要：
我们介绍DeepIR，这是一种新的热图像处理框架，它将物理上精确的传感器建模与基于深度网络的图像表示相结合。我们的关键观测结果是，热传感器捕获的图像可以被分解为缓慢变化的、与场景无关的传感器非均匀性（可以使用物理学精确建模）和场景特定的辐射通量（使用基于深度网络的正则化器很好地表示）。DeepIR既不需要训练数据，也不需要使用已知的黑体目标进行定期的地面实况校准，这使得它非常适合实际的计算机视觉任务。我们通过开发新的去噪和超分辨率算法来展示DeepIR的力量，这些算法利用了相机抖动捕获的场景的多个图像。模拟和真实数据实验表明，DeepIR可以用三张图像进行高质量的非均匀性校正，与竞争方法相比，PSNR提高了10dB。
英文摘要：
We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation. Our key enabling observations are that the images captured by thermal sensors can be factored into slowly changing, scene-independent sensor non-uniformities (that can be accurately modeled using physics) and a scene-specific radiance flux (that is well-represented using a deep network-based regularizer). DeepIR requires neither training data nor periodic ground-truth calibration with a known black body target--making it well suited for practical computer vision tasks. We demonstrate the power of going DeepIR by developing new denoising and super-resolution algorithms that exploit multiple images of the scene captured with camera jitter. Simulated and real data experiments demonstrate that DeepIR can perform high-quality non-uniformity correction with as few as three images, achieving a 10dB PSNR improvement over competing approaches.
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