论文标题
无人机的零引用摄像机的零参考图像恢复
Zero-Reference Image Restoration for Under-Display Camera of UAV
论文作者
论文摘要
在恶劣天气的影响下，无人机的裸露摄像机会摇晃，移动甚至故障，而附加设备（杜邦线）非常容易受到损坏。 我们可以在相机周围放置低成本的T-Ol覆盖层以保护它，但这也会引入图像降解问题。 特别是，大气中的温度变化会产生吸附到T-olded的雾气，这可能会在无人机的拍摄过程中造成继发性灾难（即更严重的图像降解）。 为了解决覆盖T-Oleds引起的图像降解问题，在本文中，我们提出了一种通过增强图像的纹理和颜色来增强视觉体验的新方法。 具体而言，我们的方法训练一个轻量级网络，以估计输入图像上的低级仿射网格，然后利用网格来增强块粒度处的输入图像。 我们方法的优点是不需要参考图像，并且损失函数是根据视觉体验开发的。 此外，我们的模型可以实时执行任意分辨率图像的高质量恢复。 最后，讨论了我们的模型和收集的数据集的局限性（包括白天和夜间场景）。
The exposed cameras of UAV can shake, shift, or even malfunction under the influence of harsh weather, while the add-on devices (Dupont lines) are very vulnerable to damage. We can place a low-cost T-OLED overlay around the camera to protect it, but this would also introduce image degradation issues. In particular, the temperature variations in the atmosphere can create mist that adsorbs to the T-OLED, which can cause secondary disasters (i.e., more severe image degradation) during the UAV's filming process. To solve the image degradation problem caused by overlaying T-OLEDs, in this paper we propose a new method to enhance the visual experience by enhancing the texture and color of images. Specifically, our method trains a lightweight network to estimate a low-rank affine grid on the input image, and then utilizes the grid to enhance the input image at block granularity. The advantages of our method are that no reference image is required and the loss function is developed from visual experience. In addition, our model can perform high-quality recovery of images of arbitrary resolution in real time. In the end, the limitations of our model and the collected datasets (including the daytime and nighttime scenes) are discussed.
