Interactive White Balancing for Camera-Rendered Images

• URL: http://arxiv.org/abs/2009.12632v1
• Date: Sat, 26 Sep 2020 16:22:05 GMT
• Title: Interactive White Balancing for Camera-Rendered Images
• Authors: Mahmoud Afifi and Michael S. Brown
• Abstract summary: White balance (WB) is one of the first photo-finishing steps used to render a captured image to its final output. We introduce a new framework that links the nonlinear color-mapping functions directly to user-selected colors to enable it interactive WB manipulation.
• Score: 50.08927449718674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. The proposed method is an extension of our recent work \cite{afifi2019color} that proposed a post-capture method for WB correction based on nonlinear color-mapping functions. Here, we introduce a new framework that links the nonlinear color-mapping functions directly to user-selected colors to enable {\it interactive} WB manipulation. This new framework is also more efficient in terms of memory and run-time (99\% reduction in memory and 3$\times$ speed-up). 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 results in \cite{afifi2019color}. The source code is publicly available at https://github.com/mahmoudnafifi/Interactive_WB_correction.

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