Mahmoud Afifi and Michael S. Brown
York University
Reference code for the paper Interactive White Balancing for Camera-Rendered Images Mahmoud Afifi and Michael S. Brown. In Color and Imaging Conference (CIC), 2020. If you use this code, please cite our paper:
@inproceedings{afifi2020interactive,
title={Interactive White Balancing for Camera-Rendered Images},
author={Afifi, Mahmoud and Brown, Michael S},
booktitle={Color and Imaging Conference (CIC)},
pages={},
year={2020}
}
White balance (WB) is one of the first photo-finishing steps used to render a captured image to its final output. WB is applied to remove the color cast caused by the scene's illumination. Interactive photo-editing software allows users to manually select different regions in a photo as examples of the illumination for WB correction (e.g., clicking on achromatic objects). Such interactive editing is possible only with images saved in a raw image format. This is because raw images have no photo-rendering operations applied and photo-editing software is able to apply WB and other photo-finishing procedures to render the final image. Interactively editing WB in camera-rendered images is significantly more challenging. This is because the camera hardware has already applied WB to the image and subsequent nonlinear photo-processing routines. These nonlinear rendering operations make it difficult to change the WB post-capture. The goal of this paper is to allow interactive WB manipulation of camera-rendered images. This approach is an extension to our recent work that proposed a post-capture method for WB correction based on nonlinear color-mapping functions. We introduce a new framework that is able to link the nonlinear color-mapping functions directly to the user's selected colors to allow interactive WB manipulation. Lastly, we describe how our framework can leverage a simple illumination estimation method (i.e., gray-world) to perform auto-WB correction that is on a par with the WB correction achieved by the state-of-the-art methods.
Check generateModel.m to re-generate our model.
Before running other codes, run install_.m.
The code in demo.m and demo_images.m perform auto WB using gray-world initial estimation with our rectification function.
Run GUI/main.m to interactively manipulate the WB of your photos.
This work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License.
- When Color Constancy Goes Wrong: The first work to directly address the problem of incorrectly white-balanced images; requires a small memory overhead and it is fast (CVPR 2019).
- Deep White-Balance Editing: A multi-task deep learning model for post-capture white-balance correction and editing (CVPR 2020).
- White-Balance Augmenter: An augmentation technique based on camera WB errors (ICCV 2019).