基于PCA的非局部聚类稀疏表示图像重建方法

针对腐化图像恢复不足的问题,提出一种基于PCA的非局部聚类稀疏表示模型.首先,用图像非局部自相似性来取得稀疏系数值;然后,对观测图像的稀疏编码系数进行集中聚类;最后,通过学习字典使降噪图像的稀疏编码系数接近原始图像的编码系数.实验结果表明,提出的方法在重建图像性能上较同类方法有显著提高,获得了更好的图像恢复质量.

Nonlocal Clustering Based on PCA Sparse Representation of Image Reconstruction Method

This passage puts forward a nonlocal PCA based clustering the sparse representation, to solve the shortages problem of corrupt image restoration. First of all, obtain sparse coefficient value by image nonlocal self-similarity, and then centralize the sparse coding coefficients of the observed image to sparse coefficient value. In the end, make the sparse coding coefficients of the degraded image as close as possible to those of the unknown original image through learning the dictionary. Experimental results show that the method that proposed by this passage achieves significant improvements over the previous sparse reconstructed image methods and obtains better quality of image restoration.