独立成分分析及其在图像处理中的应用

Independent component analysis and its application to image processing

独立成分分析是一种新的信号处理技术,在数字图像处理的诸多方向均表现出其独特性。对独立成分分析(ICA,Independent Component Analysis)及其在图像处理中的应用进行了综述。简要介绍了独立成分分析的数学模型,给出了极大化非高斯性的ICA估计方法、极大似然ICA估计方法、极小化互信息ICA估计方法的目标函数及其优化算法;对ICA在像素级图像融合、运动目标检测、人脸检测及特征提取、大脑信号和图像分析、数字水印、有噪图像分离等方向的应用研究进行了评述,进而显示ICA的应用价值和发展空间。

Independent component analysis is a new signal processing technology that shows specificity in digital im- age processing and so on. The principle and applications of the ICA are introduced. Mathematics model of ICA and some estimate methods are evaluated, including maximum Gaussian of ICA, minimum likelihood of ICA, and minimum mutual information of ICA. The ICA＇s applications in image fusion, moving object detection, face detection and feature extrac- tion, brain signal and image analysis, digital watermark, noisy image separation are discussed. The using value and de- velopment space of ICA is fully represented.