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Interactive White Balancing for Camera-Rendered Images

Mahmoud Afifi and Michael S. Brown

York University

teaser

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}
}

Abstract

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.

main

Get Started

Check generateModel.m to re-generate our model.

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.

License

This work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License.

Related Research Projects

  • sRGB Image White Balancing:
    • When Color Constancy Goes Wrong: The first work for white-balancing camera-rendered sRGB images (CVPR 2019).
    • White-Balance Augmenter: Emulating white-balance effects for color augmentation; it improves the accuracy of image classification and image semantic segmentation methods (ICCV 2019).
    • Color Temperature Tuning: A camera pipeline that allows accurate post-capture white-balance editing (CIC best paper award, 2019).
    • Deep White-Balance Editing: A multi-task deep learning model for post-capture white-balance editing (CVPR 2020).
  • Raw Image White Balancing:
    • APAP Bias Correction: A locally adaptive bias correction technique for illuminant estimation (JOSA A 2019).
    • SIIE: A sensor-independent deep learning framework for illumination estimation (BMVC 2019).
    • C5: A self-calibration method for cross-camera illuminant estimation (arXiv 2020).
  • Image Enhancement:
    • CIE XYZ Net: Image linearization for low-level computer vision tasks; e.g., denoising, deblurring, and image enhancement (arXiv 2020).
    • Exposure Correction: A coarse-to-fine deep learning model with adversarial training to correct badly-exposed photographs (CVPR 2021).
  • Image Manipulation:
    • MPB: Image blending using a two-stage Poisson blending (CVM 2016).
    • Image Recoloring: A fully automated image recoloring with no target/reference images (Eurographics 2019).
    • Image Relighting: Relighting using a uniformly-lit white-balanced version of input images (Runner-Up Award overall tracks of AIM 2020 challenge for image relighting, ECCV Workshops 2020).
    • HistoGAN: Controlling colors of GAN-generated images based on features derived directly from color histograms (CVPR 2021).

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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.

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