Michael S. Brown

Mahmoud Afifi, Michael S. Brown

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.

Lilika Makabe, Hiroaki Santo, Fumio Okura et al.

This paper introduces a practical and accurate calibration method for camera spectral sensitivity using a diffraction grating. Accurate calibration of camera spectral sensitivity is crucial for various computer vision tasks, including color correction, illumination estimation, and material analysis. Unlike existing approaches that require specialized narrow-band filters or reference targets with known spectral reflectances, our method only requires an uncalibrated diffraction grating sheet, readily available off-the-shelf. By capturing images of the direct illumination and its diffracted pattern through the grating sheet, our method estimates both the camera spectral sensitivity and the diffraction grating parameters in a closed-form manner. Experiments on synthetic and real-world data demonstrate that our method outperforms conventional reference target-based methods, underscoring its effectiveness and practicality.