基于白平衡修正的室外光照估计算法

Outdoor Lighting Estimation Algorithm Based on White Balance Correction

针对不同天气情况下同一太阳位置拍摄的室外场景图像,提出了一种无需用户交互的快速估计室外光照参数的算法。该算法首先通过K-means算法检测阴影区域来获取初始天空光参数,同时使用Grey-World算法获取初始太阳光参数,进而进行基图像求解,并利用白平衡修正对基图像进行校正,从而迭代优化出更准确的光照参数。实验数据表明,所提算法得到的重构图像比现有算法得到的重构图像误差更小。与现有算法相比,该算法更为快速、方便,且准确性更高,可以很好地应用于增强现实。

This paper proposed a fast algorithm for estimating outdoor illumination parameters without user interaction for outdoor scene images taken from the same sun position under different weather conditions.K-means algorithm is used to detect the shadow area to obtain the initial sky light parameters,and Grey-World algorithm is used to obtain the initial sun light parameters.Then the base image is solved,and the white balance correction is used to correct the base image,so as to iteratively optimize the illumination parameters more accurately.Experimental results show that the reconstructed image obtained by the proposed algorithm has less error than the reconstructed image obtained by the exi-sting algorithm.Compared with the existing algorithms,the proposed algorithm is faster,more convenient and more accurate.So it can be well applied to augmented reality.