彩色夜视技术可以将微光/红外双谱图像融合成一幅适于人眼观察的彩色图像, 而恰当的场景解析方法能够对彩色夜视图像的内容做出自动化分析, 进一步减轻人眼的观测负担。针对彩色夜视场景丰富多变、对算法灵活性要求高的特点, 提出了一种可在线扩展的场景解析方法。该方法基于非参数模型, 预测景物类别时不需要训练过程, 只需要使用数据库中具有语义标记的样本图像, 通过将待解析图像与样本图像进行全局及局部匹配来实现语义标签的传递。而且, 数据库可以根据应用场景的不同随时进行动态扩充。实验结果表明: 该方法在包含城市、乡野等多种场景的夜视图像上, 以及由统计色彩映射、TNO、NRL等多种融合方法得到的、具有不同色彩表征的彩色夜视图像上都具有令人满意的准确率。

Color night vision technology is able to fuse the dual-spectral image of low-light-level and infrared image into a color one suited to human observation. Furthermore, a appropriate scene parsing method on the color night vision image could additionally facilitate human observation by providing automatic content analysis. An online scalable scene parsing method was proposed aiming at the rich and changeable color night vision in practice which required algorithms with high flexibility. The proposed method was based on a non-parametric model that needed no training process when predicting scene categories. It matched the query image and the sample images in database using both global and local features, and then transfered semantic labels of the best-match samples to the query image. Moreover, the database can be dynamically expanded according to different usage scenarios. The experimental results show that proposed method achieves satisfactory accuracies on color night vision images that obtained by a variety of color night vision methods, including the statistical color mapping, TNO, and NRL, throughout diverse scenes, including cities, countryside and others.Markov random field