针对早期的滤波方法, 如线性的有高斯滤波、均值滤波、方框滤波等和非线性的如中值滤波、开闭运算等传统滤波方法是在像素级进行行列式的循环运算, 运算繁琐, 数据亢余和不能有效压缩图像进行数字化传播的缺点, 提出一种基于PCA主成分图像融合后的K-SVD滤波方法的研究, 有效弥补了单一K-SVD对椒盐噪声起不到良好滤波的缺点。首先对源图像进行多次的观测得到N幅含噪图像(既含有高斯噪声也含有椒盐噪声, 都是加性噪声)。再对N幅含噪图像进行PCA主成分提取融合后进行K-SVD滤波(如果先进行K-SVD滤波的话会造成多幅图像的K-SVD的滤波, 导致效率低且运算度冗余N倍)。这样有效消除了高斯噪声的干扰, 还解决了K-SVD对椒盐噪声不敏感的缺陷, 完成了在图像特征级数据去噪的研究。

For early filtering method, such as linear and nonlinear filtering, linear filtering includes Gaussian, mean and box filtering, nonlinear filtering includes median filtering and closed operation, which are determinant cycle operation at pixel level. According to the disadvantages of the traditional filtering methods mentioned of complicated calculation, data redundancy and image cannot be compressed effectively to perform digital transmission, a K-SVD filtering method based on principal component analysis (PCA) image fusion is proposed. And the disadvantages of single K-SVD such as without better filtering effect on salt and pepper noise is compensated effectively. N frames of images with noise are obtained through observing the source image many times, which have Gaussian and salt and pepper noise, both are additive noise. K-SVD filtering is performed after PCA extracting and fusing to N frames of images with noise. If K-SVD filtering is performed before PCA, the K-SVD filtering of multi-frame images is produced, which will lead to low efficiency and N times of calculation redundancy. So Gaussian noise interference is eliminated effectively and the disadvantage of being not sensitive to salt and pepper noise of K-SVD is resolved. And the research on data denosing at image feature level is finished.