Available at: https://digitalcommons.calpoly.edu/theses/2997
Date of Award
5-2025
Degree Name
MS in Electrical Engineering
Department/Program
Electrical Engineering
College
College of Engineering
Advisor
Jane Zhang
Advisor Department
Electrical Engineering
Advisor College
College of Engineering
Abstract
Automatic white balancing (AWB) aims to correct color casts caused by varying illumination conditions, typically assuming access to RAW sensor data. However, many real-world applications involve only sRGB images that have already been processed by in-camera pipelines. In these cases, traditional AWB algorithms often underperform due to the nonlinear transformations done by these pipelines.
This thesis builds upon a data-driven color correction framework introduced by Afifi et al. that relies on RGB-UV histograms and learned color transforms. A revised automatic white balancing (AWB) framework that improves both color accuracy and runtime efficiency is proposed. A fallback routine is implemented to improve correction quality in failure cases where the original polynomial model performs poorly. A more efficient histogram computation is also performed using subsampling, which drastically reduces the computational cost with minimal impact on visual quality.
The proposed method is evaluated with the Cube+ dataset, where it achieves a lower average color difference error across all tested metrics and fewer extreme correction failures compared to the original method, while also running significantly faster.