光学学报, 2018, 38 (2): 0233001, 网络出版: 2018-08-30   

对数域中基于实例学习的光照估计 下载: 977次

Illumination Estimation Based on Exemplar Learning in Logarithm Domain
作者单位
合肥工业大学计算机与信息学院, 安徽 合肥 230009
摘要
复杂和多光照场景下的光照估计是颜色恒常性计算的难点和热点。提出一种对数域中基于实例学习的光照估计方法。通过分析光照对图像色度的影响,提取对数色度直方图作为光照一致性特征,在实例学习框架下,根据特征相似的已知光照实例估计目标场景光照。算法分割原始图像为多个光照均匀场景,分区域估计局部光照,并融合得到整幅图像的全局光照信息。在多组单光照和多光照数据集上的实验结果表明,相较于其他先进方法,本文方法在不同数据集上的光照估计误差中位数降低了5%~14%,精度更高且稳健性更好。
Abstract
Illumination estimation in complex and multi-illumination scenes is a difficult and hot point in computational color constancy field. An illumination estimation algorithm based on exemplar learning in the logarithm domain is proposed. The effects of illumination on chrominance of an image are studied, and the log-chrominance histogram is extracted as the illumination consistency feature. The frame of exemplar learning is introduced, and the illumination of target scenes is estimated by known-illumination exemplars with similar features. Image segmentation is applied by the algorithm firstly, then illumination estimation is performed for each segment independently, and segmental illuminations are fused together to calculate the illumination for the whole image. Experiments are carried out on several single illumination and multiple illumination data sets. The experimental results show that compared with other advance methods, the proposed method reduces the median error of the illumination estimation by 5%-14% with higher accuracy and higher robustness.

崔帅, 张骏, 高隽. 对数域中基于实例学习的光照估计[J]. 光学学报, 2018, 38(2): 0233001. Shuai Cui, Jun Zhang, Jun Gao. Illumination Estimation Based on Exemplar Learning in Logarithm Domain[J]. Acta Optica Sinica, 2018, 38(2): 0233001.

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