结合第二代Bandelet变换分块的字典学习图像去噪算法

针对以往稀疏编码在图像去噪过程中存在的噪声残留和缺乏对图像的边缘与细节的本质特征的保护等问题,提出了一种结合第二代Bandelet变换分块的字典学习图像去噪算法,其更好地利用了图像的几何特性进行去噪。首先,通过第二代Bandelet变换可以灵活地根据图像几何流的正则性特征并能够自适应地获得图像的最稀疏表示来准确估计图像信息,并能自适应地选择最优的几何方向；然后,根据K-奇异值分解(K-Singular Value Decomposition,K-SVD)算法来训练学习字典；最后,通过四叉树分割对噪声图像进行自适应分块,从而去除噪声并保护图像的边缘与细节。实验结果表明,相比于其他学习字典,所提算法能更有效地保留图像的边缘特征与图像的精细结构。

Dictionary Learning Image Denoising Algorithm Combining Second Generation Bandelet Transform Block

There are mainly three challenges for sparse coding in the process of image denoising,including incomplete image denoising,the noise residue,and the lack of protection of image edges and detailed characteristics.This paper proposed a dictionary learning image denoising algorithm combining the second generation Bandelet transformation block method to achieve better removal of noise.With the second generation Bandelet transformation,the sparse representation of images can be automatically obtained to accurately estimate the image information according to the regularity of the image geometry manifold.The K-singular value decomposition (K-SVD) algorithm is used to learn the dictionary under the moderate Gaussian white noise variance.Moreover,it utilizes the quadtree segmentation to adaptively predict the noise images and segment images into blocks.Experimental results show that the proposed method can effectively preserve the edge features of image and the fine structure of image while removing the noise.Since it employs the second generation Bandelet transformation for segmentation,the algorithm structure is well optimized and the operational efficiency is also improved.