非局部均值噪声预测的独立成分分析降噪研究

为解决采用独立成分分析算法进行图像降噪需要多个观测信号的问题,提出一种对单张图像冗余信息进行稀疏以生成多个观测信号的方法。该方法首先采用字典压缩算法对原噪声图像稀疏;再采用非局部均值算法对压缩图像的冗余信息进行处理,将处理后的冗余信息生成初次降噪图像;将初次降噪图像和原噪声图像共同作为独立成分分析的多个观测信号。结合非局部均值算法可以避免仅使用字典压缩算法造成的过量稀疏,研究表明当高斯白噪声标准差σ在20~45范围时,本文提出的方法比字典稀疏压缩算法和非局部均值算法降噪效果更好,图像降噪后的峰值信噪比是降噪前的1.4倍。本文提出的方法在高斯白噪声标准差σ在20~45范围时,具有很好的降噪效果。

Noise reduction of independent component analysis based on NLmeans noise prediction

It is well known that multiple observed signals are required for image denoising with ICA(Independent Component Analysis). In this paper, a method that multiple observations were generated by making the reduntant imformation of a single image sparse was presented. Firstly, made the only one noisy image to be sparse by using the dictionary compression algorithm of K-SVD (Kernel Singular Value Decomposition). Secondly, obtained the first-time denoised image by using the redundant information. Finally, made both the first-time denoised image and original noisy image as the multiple observations for ICA separation. It could be seen that the sparse image obtained by proposed method was more exact than that by using only a dictionary compression algorithm of Nlmeans (Non-Local means). The result obtained shows that when the Gauss white noise's standard deviation σ is in the range of 20-45, the proposed method is better than either K-SVD algorithm or NLmeans algorithm, and the denoised image's PSNR (peak signal to noise ratio) is 1.4 times larger than that of the original noisy image.