米字型各向异性扩散模型的图像去噪算法

Improved image denoising algorithm using UK-flag shaped anisotropic diffusion model

针对原始的各向异性扩散模型在对带噪图像去噪时,只利用了邻域内东、南、西、北4个方向上的参考信息,使得去噪效果不够明显的问题,提出了米字型各向异性扩散模型的图像去噪算法。该算法在利用了原始算法中待修复点周围4个方向上参考信息的基础上,还引入了该点邻域内对角线方向上的新信息,给出了采用周围8个方向上的信息进行对图像去噪的新模型,同时证明了该模型的合理性。用新提出的算法与原算法以及一种改进的同类算法对4幅带噪图像进行去噪。实验结果表明,新提出算法去噪效果的峰值信噪比(PSNR)相比原算法和改进同类算法平均提高1.90 dB和1.43 dB,平均结构相似度(MSSIM)分别平均提高0.175和0.1,说明该算法更适合于图像去噪。

To effectively improve the denoising effect of the original anisotropic diffusion model that used only the 4 neighborhood pixels information and ignored the diagonal neighborhood pixels information of the pixel to be repaired in the image denoising process, a image denoising algorithm using UK-flag shaped anisotropic diffusion model was proposed. This model not only made full use of the reference information of the 4 neighborhood pixels as in original algorithm, but also used another 4 diagonal neighborhood pixels information in the denoising process. Then the model using the 8 direction pixels information for image denoising was presented, and it was proved to be rational. The proposed algorithm, the original algorithm, and an improved similar algorithm were used to remove the noise from 4 images with noise. The experimental results show that the proposed algorithm has an average increase of 1.90 dB and 1.43 dB in Peak Signal-to-Noise Ratio （PSNR） value respectively, and an average increase of 0. 175 and 0. 1 in Mean Structure Similitary Index （MSSIM） value respectively, compared with the original algorithm and the improved similar algorithm, which concludes that the proposed algorithm is more suitable for image denoising.