CAJ | 학술논문

主成分分析（PCA）是一种将多个变量通过线性变换选出较少个数重要变量的一种多元统计方法。在图像去噪中，由于图像的局部相似性，提出一种新的有效的去除噪声的算法。通过块匹配法寻找出相似块作为训练样本，利用主成分分析提取信号的主要特征，然后根据统计理论中最小均方误差方法构造线性自适应阈值方程，对含噪图像的每一块进行自适应阈值去噪。实验结果表明，该方法能有效去除图像的高斯白噪声，并同时能很好的保持边缘等的细节信息。
주성분분석（PCA）시일충장다개변량통과선성변환선출교소개수중요변량적일충다원통계방법。재도상거조중，유우도상적국부상사성，제출일충신적유효적거제조성적산법。통과괴필배법심조출상사괴작위훈련양본，이용주성분분석제취신호적주요특정，연후근거통계이론중최소균방오차방법구조선성자괄응역치방정，대함조도상적매일괴진행자괄응역치거조。실험결과표명，해방법능유효거제도상적고사백조성，병동시능흔호적보지변연등적세절신식。
Principal component analysis(PCA) is a multivariate statistical method which selects a few important variables through a linear transformation of Multiple variables. In image denoising, because of the local similarity of images, a new and effective noise removal algorithm is put forwarded. The similar blocks are found out as training samples by block matching algorithm and the main signal feature extraction is extracted by PCA, and then, an adaptive threshold is used to each denoised block to remove noise. The experimental results show that the method can effectively remove the image of Gauss white noise, and at the same time, can be very good to keep the edge detail information.