采用交替K-奇异值分解字典训练的图像超分辨率算法

Image Super-resolution Based on Alternate K-Singular Value Decomposition

采用稀疏表示的图像超分辨率算法中,双字典训练算法与字典的细节恢复能力相关,针对已有双字典训练算法使字典缺乏高频细节信息的特点,提出了一种交替K-奇异值分解字典训练算法。该算法分为训练和测试部分。在训练部分每次字典更新都采用奇异值分解所得到的向量对低高频样本块进行最佳低秩逼近,使得低高频样本块随着迭代次数的增加逐渐取得相同或者相似的稀疏表示系数。在测试过程中,测试低频样本块可以利用低频字典取得的稀疏表示系数与高频字典相乘得到高频细节信息。实验表明,与目前已有算法相比,该算法能够得到高频细节较丰富的图像,平均峰值信噪提高0.3dB以上,结构相似度提高0.01左右。

The coupled dictionary training algorithm in super-resolution based on sparse representation are directly related to the detail recovery capability of the algorithm,but the existing algorithm makes the dictionaries lack texture structure information.This paper proposes an alternate K-singular value decomposition dictionary training algorithm.This algorithm is composed of a training stage and a testing stage.In the training stage,the best low rank approximations of low and high frequency patches are used for the updating of the dictionaries.This method makes the sparse representations of low and high frequency patches becomes more and more similar with the increasing of the iteration number.In the testing stages,the high frequency details can be estimated by multiplying the sparse representations generated with low frequency patches with the high frequency dictionary.The experimental results demonstrate that the proposed algorithm can provide clearer resultant images.Compared with many existing methods,the average peak signal to noise ratio exceeds about 0.3dB and structure similarity exceeds about 0.1.