基于小波域稀疏表示和自适应混合样本回归的图像超分辨率重建算法

Image super-resolution reconstruction algorithm based on image sparse representation in wavelet domain and adaptive mixed sample regression

针对局部特征不能较好地在空域表示的缺点,对训练集进行直接的小波变换,在训练阶段采用K-SVD字典学习算法对提取的小波域高低分辨率特征分别训练四个子带高低分辨率字典对,并把所得子带字典用于小波域高分辨率图像重建.为了进一步提升重建图像的质量,提出一个自适应混合样本脊回归模型(AMSRR)用于调制重建图像的高频成分.实验结果表明,本文提出的算法在视觉效果以及量化指标(PSNR,SSIM)上优于对比的空域方法.

Aimed at the defect that the local characteristics are hard to be well represented in spatial domain,the training set is directively transformed into wavelet domain.In training phase,the K-SVD dictionary learning algorithm is used to train four pairs of dictionaries of subband high-low resolution respectively for high-low resolution feature in extracted wavelet domain,and the subband dictionaries obtained are used for high-resolution image reconstruction in wavelet domain.In order to improve the quality of the reconstructed image,an adaptive mixed sample ridge regression（AMSRR）model is proposed to modulate the high-frequency component of the image.Massive experimental results show that the proposed algorithm will be superior to the competitive spatial domain methods both in visual effect and quantification index（PSNR and SSIM）.