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 photorendering 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 [6] 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 interactive WB manipulation. This new framework is also more efficient in terms of memory and run-time (99% reduction in memory and 3 × 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 [6].
Illuminant color estimation method for the scene illuminated by several illuminants is proposed. In the past, most of the conventional methods focused on the scene under one illuminant and assumed the color of the illuminant is constant throughout the scene, however, these are not always the case. These days, the methods for estimating several colors of the scene illuminants are studied. Some of them are based on dichromatic reflection model, and assume that colors of the illuminant in the small region in the image is only one and constant throughout the region. Proposed method is based on Gray-World assumption, which doesn't need the specular reflection components but variety of colors in the image. In the method, image is divided into small regions and the illuminant colors are estimated for the small regions after the judgement whether each small region in the image includes variety of colors is or not. By clustering the estimation for each small region, final result is derived. In the experiments, simulated images under two illuminants are used and the estimated results are evaluated. The results show that the estimation error by the proposed method is smaller than those by the conventional one.
Recently, a remarkably simple method was developed to solve the illumination and reflectance spectra separation problem (IRSS) based on the standard low-dimensionality assumption of reflectance. However, because this method assumes the scene is under one uniform illumination, it can not handle scene contains multiple illuminations or dominant shadows. In this paper, we address this problem by formulating the multiple illuminations and reflectance separation problem as a Conditional Random Field (CRF) optimization task over local separations. We then improve local illumination and reflectance separation by incorporating spatial information in each local patch.
This research examined the performance of skin coloredpatches for accurately estimating human skin color. More than 300 facial images of Korean females were taken with a digital singlelens reflex camera (Canon 550D) while each was holding the X-Rite Digital ColorChecker® semi-gloss target. The color checker consisted of 140 color patches, including the 14 skin-colored ones. As the ground truth, the CIE 1976 L*a* b* values of seven spots in each face were measured with a spectrophotometer. For an examination, three sets of calibration targets were compared, and each set consisted of the whole 140 patches, 24 standard color patches and 14 skin-colored patches. Consequently, three sets of estimated skin colors were obtained, and the errors from the ground truth were calculated through the square root of the sum of squared differences (ΔE). The results show that the error of color correction using the 14 skin-colored patches was significantly smaller (average ΔE = 8.58, SD = 3.89) than errors of correction using the other two sets of color patches. The study provides evidence that the skin-colored patches support more accurate estimations of skin colors. It is expected that the skin-colored patches will perform as a new standard calibration target for skin-related image calibration.