Namespace Emgu.CV.XPhoto

GrayworldWB: Gray-world white balance algorithm. This algorithm scales the values of pixels based on a gray-world assumption which states that the average of all channels should result in a gray image. It adds a modification which thresholds pixels based on their saturation value and only uses pixels below the provided threshold in finding average pixel values. Saturation is calculated using the following for a 3-channel RGB image per pixel I and is in the range [0, 1]: Saturation[I]= max(R,G,B)-min(R,G,B) / max(R,G,B) A threshold of 1 means that all pixels are used to white-balance, while a threshold of 0 means no pixels are used. Lower thresholds are useful in white-balancing saturated images. Currently supports images of type CV_8UC3 and CV_16UC3.

LearningBasedWB: More sophisticated learning-based automatic white balance algorithm. As GrayworldWB, this algorithm works by applying different gains to the input image channels, but their computation is a bit more involved compared to the simple gray-world assumption. More details about the algorithm can be found in: Dongliang Cheng, Brian Price, Scott Cohen, and Michael S Brown. Effective learning-based illuminant estimation using simple features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1000-1008, 2015. To mask out saturated pixels this function uses only pixels that satisfy the following condition: max(R,G,B) / range_max_val < saturation_thresh Currently supports images of type CV_8UC3 and CV_16UC3.

SimpleWB: A simple white balance algorithm that works by independently stretching each of the input image channels to the specified range. For increased robustness it ignores the top and bottom p% of pixel values.

TonemapDurand: This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter and compresses contrast of the base layer thus preserving all the details. This implementation uses regular bilateral filter from opencv.