Physics-based colour models in computer vision, colour science, graphics and multimedia

这项工作的目的是在成像的物理为基础的概念。所描述的项目组件是具体的，但形成了整个计算机视觉长期存在和深层问题的各个方面。 这项研究的背景是图像的“融合”。 例如，假设我们需要一个彩色图像的灰度 *（“黑色和白色”）复印件：这里我们的目标是将三个RGB（红色，绿色，蓝色）颜色通道融合成一个单一的灰色输出。 这是一个看似困难的问题，关于这个问题已经发表了数百篇学术论文。 许多其他问题域也是类似的：例如，*将RGB和近红外（NIR）融合成一个新的彩色图像，具有更好的夜视 * 特性，适合在黑暗中驾驶。据了解，在相机研究 * 社区，包括近红外传感器在一个领先品牌的智能手机是关于 * 发生。事实证明，“对比度”的概念对于图像融合至关重要--我们需要捕捉图像集中的 * 局部变化，以便最好地联合收割机组合它们。 究竟什么是对比度 *，一直争论不休。 在我的光谱边缘（SpE）应用程序（和 * 分拆公司）中，对比度的定义来自坚实的数学基础。 事实证明，SpE算法适用于各种 * 情况：例如，在医学成像中，通常会使用不止3个维度（如RGB颜色）来开发“图像”数据 *。在卫星成像中，在每个像素位置拍摄具有数百个值的“光谱”图像是很常见的。SpE方法一方面解开了颜色和另一方面空间位置的变化如何联合收割机结合形成对比。这些计算都发生在“梯度”域中-梯度是图像在x和y方向上的 * 导数，其中成像中的导数 * 简单地表示从像素到其相邻像素的变化。 现在，这是应用数学中的一个 * 经典问题，将我们通过组合对比度得到的梯度（在 *R，G，B中的每一个中）转换回彩色图像。将梯度“重新整合”到图像中的经典解决方案可以追溯到1800年。 然而，* 该方法，即使由于在许多数学方法中阐述，* 产生的图像在输出图像中显示令人不快的伪影 *。 相反，在这个建议中，我将更深入地研究使用SPE方法，沿着一种新的计算机科学方法来将 * 重新整合到输出图像中。 到目前为止，SpE方法（和专利）* 使用适用于整个图像的数学：在这里，我提出了面向像素的 * 方法，应该产生更多的细节。开发的方法将产生深远的影响，因为基于梯度的方法现在是计算 * 摄影问题的标准方法。作为一个例子，这项研究将对将白天图像信息带入夜间监视图像的问题产生重大影响。

This work is aimed at physics-based concepts in imaging. The project***components described are specific, but form aspects of long-standing and***deep problems throughout computer vision. The context for this research***is the "fusion" of images. For example, suppose we need a greyscale***("black and white") xerox copy of a colour image: here we are aiming to***fuse the three RGB (red, geen, blue) colour channels into a single grey output. This is a***deceptively difficult problem, and hundreds of academic papers have been***published on the subject. Many other problem domains are similar: e.g.,***fusing RGB plus Near-Infrared (NIR) into a new colour image with better night-vision***properties for driving in the dark. It is understood in the camera-research***community that including a NIR sensor in a leading brand of smartphones is about***to take place.******It turns out that the idea of "contrast" is critical for image fusion -- we need to capture***local changes in the set of images to best combine them. Just what contrast***consists of has been long-debated. In my Spectral Edge (SpE) application (and***spin-off company) a definition of contrast is taken from solid mathematical***foundations. It turns out the the SpE algorithm is applicable in a variety of***situations: for example in medical imaging it is common to develop "image" data***with more than just 3 dimensions like RGB colour. And in satellite imaging it is***common to take "spectral" images with hundreds of values at each pixel location.***The SpE approach disentangles how colour, on the one hand, and change in spatial***position on the other, combine to form contrast.******These calculations all take place in the "gradient" domain -- gradient is the***derivative of the image, in both x and y directions, where derivative in imaging***simply means the change from a pixel to its neighbouring pixel. Now, it is a***classical problem in applied mathematics to transform back to a colour image ***the gradient (in each of***R, G, B) that we arrived at by combining contrast. The classic solution for***"reintegrating" the gradient back into image dates to the year 1800. However***that method, even though since elaborated in a number of mathematical approaches,***produces images that display unpleasant artifacts ***in the output image. Instead, in this proposal I will look deeper into making use***of the SpE approach, along with a new, computer-science approach to***reintegration into an output image. To date, the SpE method (and patent) ***uses mathematics that applies to a whole image: here I propose pixel-oriented***methods that should produce substantially more detail.******The methods developed will have far-reaching impact in that gradient-based***approaches are now the standard approach to problems in computational***photography. As one example, this research will have a major impact on the***problem of bringing daytime image information into nighttime surveillance***images.