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

计算机视觉、色彩科学、图形和多媒体中基于物理的色彩模型

• 批准号: RGPIN-2017-03886
• 负责人: Drew, Mark
• 金额: $ 1.89万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710198
• 关键词: Physics; based; colour; models; computer

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.

这项工作针对的是成像中基于物理的概念。该项目 所描述的组件是具体的，但形式方面的长期和 计算机视觉中存在的深层次问题。这项研究的背景 是影像的“融合”。例如，假设我们需要一个灰度 (“黑白”)彩色图像的复印件：在这里我们的目标是 将三个RGB(红、绿、蓝)颜色通道融合为单个灰色输出。这是一个 看似困难的问题，数百篇学术论文已经 发表了关于这一主题的文章。许多其他问题领域是相似的：例如， 将RGB加近红外(NIR)融合成夜视效果更好的新彩色图像 在黑暗中开车的特性。这是在相机研究中被理解的 在一个领先的智能手机品牌中加入近红外传感器的社区是关于 发生的事。 事实证明，“对比度”的概念对图像融合至关重要--我们需要捕捉 图像集合中的局部变化以最佳地组合它们。到底有什么反差 由什么组成一直是争论已久的。在我的光谱边缘(SPE)应用程序中(和 分拆公司)对比度的定义取自固体数学 基金会。结果表明，SPE算法适用于多种环境 情况：例如，在医学成像中，开发“图像”数据是很常见的 不只是像RGB颜色这样的3个维度。在卫星成像中，它是 通常拍摄每个像素位置都有数百个值的“光谱”图像。 SPE方法一方面解开了颜色和空间变化的关系 定位在另一端，结合在一起形成对比。 这些计算都是在“梯度”域中进行的--梯度是 图像在x和y方向上的导数，其中成像中的导数 简单地说，就是从一个像素到它的相邻像素的变化。现在，它是一个 应用数学中的经典问题：变换回彩色图像 渐变(在每个 R，G，B)，我们结合对比得到。的经典解决方案 将渐变重新整合到图像中的历史可以追溯到1800年。然而， 这种方法，尽管后来在许多数学方法中进行了详细阐述， 生成显示令人不快的伪像的图像 在输出图像中。相反，在这项提案中，我将更深入地研究如何使用 SPE方法以及一种新的计算机科学方法 重新生成输出图像。到目前为止，SPE方法(和专利) 使用适用于整个图像的数学方法：在此我提出面向像素 应该产生更多细节的方法。 所开发的方法将对以梯度为基础的 方法现在是解决计算中问题的标准方法 摄影。作为一个例子，这项研究将对 将日间图像信息纳入夜间监视的问题 图像。