CAJ | 학술논문

去除光照影响是人脸图像增强和识别的关键技术.二维经验模态分解(2-D EMD)可根据图像特征自适应地将其分成多个尺度的分量,各分量重建可实现图像增强或去噪的功能.基于二维EMD的原理及特征,研究了2种新的人脸图像去光照算法,即基于原始图像的直接二维EMD重建和基于Lambert光照模型的对数二维EMD重建,并运用特征脸识别对去光照算法的效果进行了测试和比较分析.仿真结果表明,运用二维EMD重建可较好地去除人脸图像中的光照影响,而对数二维EMD重建算法相对具有更明显的稳健性和适用性.
거제광조영향시인검도상증강화식별적관건기술.이유경험모태분해(2-D EMD)가근거도상특정자괄응지장기분성다개척도적분량,각분량중건가실현도상증강혹거조적공능.기우이유EMD적원리급특정,연구료2충신적인검도상거광조산법,즉기우원시도상적직접이유EMD중건화기우Lambert광조모형적대수이유EMD중건,병운용특정검식별대거광조산법적효과진행료측시화비교분석.방진결과표명,운용이유EMD중건가교호지거제인검도상중적광조영향,이대수이유EMD중건산법상대구유경명현적은건성화괄용성.
De-illumination is a key technology for enhancing and recognizing images of human faces. An image can be decomposed into several intrinsic mode functions (IMFs) adaptively by two dimensional empirical mode decomposition (2-D EMD). The reconstruction of these IMFs is useful in image denoising and enhancement. Based on the theory and features of 2-D EMD, two new kinds of de-illumination methods were explored: 2-D EMD followed by reconstruction of the original image, and 2-D EMD followed by reconstruction of a log-image based on the Lambert illumination model. A test and comparison using the eigen-face recognition method was run. The results showed good de-illumination performance by both methods, but log-images had better robustness and practicability.