基于分组字典与变分模型的图像去噪算法

针对加性高斯噪声去除问题,在现有传统的K均值奇异值分解(K-SVD)字典学习算法的基础上,提出一种将字典学习与变分模型相融合的改进算法。首先,根据图像的几何和光度信息将图像进行聚类分组,再将图像组按照边缘和纹理类别进行分类,根据噪声水平和图像组类别训练一个自适应字典;其次,将通过所学字典得到的稀疏表示先验与图像本身的非局部相似先验进行融合来构建变分模型;最后,通过求解变分模型得到去噪后图像。实验结果表明,与同类去噪算法相比,当噪声比率较高时,所提算法可以解决前期算法准确性较差、纹理丢失较为严重、产生视觉伪影等问题,在视觉效果上要更为理想;同时该算法结构相似性指数有明显提高,峰值信噪比(PSNR)的值更是平均提高了10%以上。

Image denoising algorithm based on grouped dictionaries and variational model

Aiming at problem of additive Gauss noise removal, an improved image restoration algorithm based on the existing K-means Singular Value Decomposition (K-SVD) method was proposed by integrating dictionary learning and variational model. Firstly, according to geometric and photometric information, image blocks were clustered into different groups, and these groups were classified into different types according to the texture and edge categories, then an adaptive dictionary was trained according to the types of these groups and the size of the atoms determined by the noise level. Secondly, a variational model was constructed by fusing the sparse representation priori obtained from the dictionary with the non-local similarity priori of the image itself. Finally, the final denoised image was obtained by solving the variational model. The experimental results show that compared with similar denoising algorithms, when the noise ratio is high, the proposed method has better visual effect, solving the problems of poor accuracy, serious texture loss and visual artifacts; the structural similarity index is also significantly improved, and the Peak Signal-to-Noise Ratio (PSNR) is increased by an average of more than 10%.