Modelling Image Formation and In-Camera Imaging Pipelines

我的研究项目专注于通过数码相机拍摄的图像来理解物理世界。更具体地说，我感兴趣的是一张图像能告诉我们关于现实世界环境的什么。为此，我的研究重点是如何对进入相机的物理光如何转换为最终的三通道图像(红、绿、蓝)基于像素的图像进行建模。这种类型的图像形成有时被称为“低级计算机视觉”，它处理图像是如何形成的以及它们与物理世界的关系。低级计算机视觉通常将图像视为2D信号，很像通信信号。通过这种方式，我们可以讨论一些问题，例如在经历了一些退化(例如传感器噪声或图像处理过程中的运动-例如由于相机运动导致的图像模糊)时尝试确定真实信号。这样的成像模型还可以考虑环境因素，如雾和雾霾，以及如何通过计算方法消除这些因素。

My research program is focused on understanding the physical world through images captured by digital cameras. More specifically, I'm interested in what an image can tell us about the real-world environment. To this end, my research focuses on ways to model how physical light coming into the camera is transformed into the final three-channel image (red, green, blue) pixel-based images. This type of image formation is sometimes referred to as "low-level computer vision", which deals with how images are formed and their relationship to the physical world. Low-level computer vision often treats an image as a 2D signal, much like a communication signal. In this way, we can discuss problems such as trying to determine the true signal when it has undergone some degradation (such as sensor noise, or movement during the image process – e.g. image blur due to camera motion). Such image formation models can also take into consideration environment factors, such as fog and haze, and how this can be removed through computational methods.