基于图像灰度熵的自适应字典学习算法

Adaptive Dictionary Learning Algorithm Based on Image Gray Entropy

针对传统图像稀疏表示字典学习算法仅对图像训练学习单一字典,不能很好地对包含不同图像信息的图像块进行最优稀疏表示的问题,将图像灰度熵的思想引入到字典学习算法中,提出基于图像灰度熵的自适应字典学习算法。该算法将图像库作为训练样本,对图像库图像进行分块,计算各子块的灰度熵大小,依据灰度熵大小对子块进行分类,针对不同类别子块,设定不同K-奇异值分解算法参数,分别进行字典训练,从而得到多个不同的字典。根据灰度熵大小选择训练好的字典对待表示图像子块进行稀疏表示。仿真实验及结果表明,所提算法能够对图像进行较好的稀疏表示,图像的重构效果也得到了明显提升。

Aiming at the problem that the traditional dictionary learning algorithm of image sparse representation only learns a single dictionary for image training,and can not optimally sparsely represent image blocks containing different image information,through introducing the local gray entropy of image into the dictionary learning algorithm,this paper proposed an adaptive dictionary learning algorithm based on image local gray entropy.The proposed algorithm makes use of the image database as training sample.Firstly,the image database is divided into blocks,and the gray entropy of each sub-block is calculated.Then,the sub-blocks are classified according to the size of the gray entropy,and different K-Singular Value Decomposition(K-SVD)parameters are set for different categories of sub-blocks to perform dictionary training respectively,thus obtaining a plurality of different dictionaries.Lastly,a well-trained dictionary is selected for the image sub-blocks to conduct sparse representation according to the size of the gray entropy.Simulation experiment results show that the proposed algorithm can sparsely represent the images better,and the effect of image reconstruction is also improved significantly.