Opt Express. 2021 Apr 12;29(8):11947-11961. doi: 10.1364/OE.420069.
Spectral response (or sensitivity) functions of a three-color image sensor (or trichromatic camera) allow a mapping from spectral stimuli to RGB color values. Like biological photosensors, digital RGB spectral responses are device dependent and significantly vary from model to model. Thus, the information on the RGB spectral response functions of a specific device is vital in a variety of computer vision as well as mobile health (mHealth) applications. Theoretically, spectral response functions can directly be measured with sophisticated calibration equipment in a specialized laboratory setting, which is not easily accessible for most application developers. As a result, several mathematical methods have been proposed relying on standard color references. Typical optimization frameworks with constraints are often complicated, requiring a large number of colors. We report a compressive sensing framework in the frequency domain for accurately predicting RGB spectral response functions only with several primary colors. Using a scientific camera, we first validate the estimation method with direct spectral sensitivity measurements and ensure that the root mean square errors between the ground truth and recovered RGB spectral response functions are negligible. We further recover the RGB spectral response functions of smartphones and validate with an expanded color checker reference. We expect that this simple yet reliable estimation method of RGB spectral sensitivity can easily be applied for color calibration and standardization in machine vision, hyperspectral filters, and mHealth applications that capitalize on the built-in cameras of smartphones.
三色图像传感器（或三基色相机）的光谱响应（或灵敏度）函数允许将光谱刺激映射到 RGB 颜色值。与生物光传感器类似，数字 RGB 光谱响应取决于设备，并且在不同型号之间差异很大。因此，特定设备的 RGB 光谱响应函数的信息在各种计算机视觉以及移动健康（mHealth）应用中至关重要。从理论上讲，可以在专门的实验室环境中使用复杂的校准设备直接测量光谱响应函数，但这对于大多数应用程序开发人员来说并不容易。因此，已经提出了几种基于标准颜色参考的数学方法。具有约束的典型优化框架通常很复杂，需要大量颜色。我们报告了一种频域压缩感知框架，仅使用几种原色即可准确预测 RGB 光谱响应函数。我们首先使用科学相机通过直接光谱灵敏度测量来验证估计方法，并确保真实 RGB 光谱响应函数和恢复的 RGB 光谱响应函数之间的均方根误差可以忽略不计。我们进一步恢复智能手机的 RGB 光谱响应函数，并使用扩展的颜色检查器参考进行验证。我们希望这种简单可靠的 RGB 光谱灵敏度估计方法可以轻松应用于机器视觉、高光谱滤波器和利用智能手机内置摄像头的 mHealth 应用中的颜色校准和标准化。
