Camera Color Correction Toolbox makes it easy to calculate the optimal color correction matrix between the camera responses and the targets by minimizing nonlinear loss function. This toolbox can seamlessly cooperate with other modules in the Image Signal Processing pipeline like spatial nonuniformity correction, white-balancing, etc.
Following color correction models are supported:
- Linear transformation
- Polynomial regression
- Root-polynomial regression
MATLAB version R2018b or higher with Image Processing Toolbox is recommended in order to implement images.roi object. But for other versions the normal rectangle function works well too. Optimization Toolbox is required.
- This gif is only for demonstration purpose. The source image suffered from lots of color degradation, that is why the result looks very bad 😂
- Please see
/demo/demo.m
for a more detailed explanation.
- Hong, Guowei, M. Ronnier Luo, and Peter A. Rhodes. "A study of digital camera colorimetric characterization based on polynomial modeling." Color Research & Application: Endorsed by Inter‐Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Français de la Couleur 26.1 (2001): 76-84.
- Finlayson, Graham D., Michal Mackiewicz, and Anya Hurlbert. "Color correction using root-polynomial regression." IEEE Transactions on Image Processing 24.5 (2015): 1460-1470.
Copyright 2019 Qiu Jueqin
Licensed under MIT.