Joelle Dowling
Introduction
Color Calibration is one piece of simulating the image processing pipeline. Without color calibration, or the camera sensor's spectral sensitivity, we would not know if we are accurately simulating the colors of the sensor image.
In past work, accurate simulated spectral quantum efficiencies were achieved. However, the data used to generate this model is tedious to gather. For example, the necessary data was generated by measuring the radiance of many different monochromatic light sources.
The goal of this project is to achieve a similarly accurate model generated using data that is much quicker to gather.
The purpose of this project is to model the spectral quantum efficiency (QE) of a camera sensor. Accurately modelling this can help in validation on other projects. In the past, this model has been made with radiance data generated by many different monochromatic light sources. In this project, the radiance data is generated by measuring a Macbeth ColorChecker (MCC) under 3 different illuminants with the Photoresearch spectrophotometer, model PR670.
Background
Methods
The setup of this project has two parts. The first is to generate a model for the Google Pixel 4A's camera sensor's spectral quantum efficiency. We do this using the
This project multiple methods to improve the spectral quantum efficiency model. To initially solve for the spectral quantum efficiency matrix, the linear equation explained in the previous section was used. However, doing this alone is not sufficient to get an accurate model. This is the case, in part, because the radiance data was generated by measuring the radiance of MCC. Our data technically has 72 samples (24 per MCC x 3 illuminants), but the patches are not independent. Singular value decomposition (SVD) can be used to obtain the principal components of our samples, which results in less than 10 independent measurements. Since we are trying to get the spectral QE information for 31 wavelengths (400:10:700 nm), we are heavily under-sampled.
Furthermore, simply solving the linear equation will create an overfitted model. We can use the results of the SVD to make a new basis and represent the spectral QE as a weighted sum of the new basis. Lastly, if we believe that the camera's spectral QE is similar to Sony's characterization of the