在图像处理领域, 基于稀疏表示理论的图像超分辨力算法、高低分辨力字典与稀疏编码之间的映射关系是其中的 2个关键环节。由于丰富多样的图像类型, 单一字典并不能很好地表示图像。而在稀疏编码之间的映射关系上, 严格相等的约束关系也限制了图像重建的效果。针对上述两个方面, 采用包容性更强的多个字典与约束条件更为宽松的全耦合稀疏关系进行图像的超分辨力重建。在图像非局部自相似性的基础上, 进行多次自适应聚类; 挑选出最优的聚类, 通过全耦合稀疏学习的图像超分辨力算法, 得到多个字典; 最后, 对输入的低分辨力图像进行分类重建, 得到高分辨力图片。实验结果表明, 在图像 Leaves,Barbara,Room上, 本文的聚类算法比原全耦合稀疏学习算法在峰值信噪比 (PSNR)上分别提升了 0.51 dB,0.21 dB,0.15 dB。

In the field of image processing, dictionary learning and the mapping from Low-Resolution (LR) image to High-Resolution(HR) image are two important components of image Super-Resolution(SR) algorithms based on sparse representation theory. Due to the rich and varied image types, a single dictionary does not represent the image very well. And the strict equaling to the mapping between LR and HR images also limits the image reconstruction effect. From above two aspects, more inclusive multi-dictionary and the more relaxed coupled dictionary sparse learning algorithms are adopted to perform SR reconstruction of the image. First of all, the method performs multiple adaptive clustering on the basis of non-local self similarity of images in this paper. Secondly, the optimal clustering is selected, and the dictionary is got by the coupling dictionary sparse learning algorithm. Finally, the input LR images are classified and reconstructed to obtain HR images. The experimental results show that Peak Signal to Noise Ratios(PSNRs) of image Leaves, Barbara and Room with the proposed clustering algorithm is higher than that with original sparse learning algorithm by 0.51 dB, 0.21 dB, 0.15 dB respectively.