一种自适应组稀疏表示的图像修复方法

Self-adaptive Group Sparse Representation Method for Image Inpainting

针对组稀疏表示图像修复方法采用固定大小的图像块,致使修复结果中存在纹理和结构清晰性较差的问题,提出一种基于自适应组稀疏表示的图像修复方法。由于自然图像中纹理和结构信息不同,为了与原方法固定图像块大小的组结构作区分,首先提出一种自适应选取样本图像块大小的方法来构造自适应的组结构;然后以组为单位对其进行奇异值分解,获得该图像块组的自适应学习字典,并利用分裂伯格曼迭代(Split Bregman Iteration)算法求解目标代价函数;最后通过调整组中的图像块数量和迭代次数对每个组的自适应字典和稀疏编码系数进行更新,以获取较好的修复效果。实验结果表明,该方法不仅在峰值信噪比和特征相似性度量上有所提高,同时也提高了修复效率。

This paper proposed an image inpainting algorithm based on self-adaptive group sparse representation to solve the problem that the texture and structure clarity are poor in the repair results.Due to the difference of texture and structure information in the natural images,in order to distinguish the group structure with the fixed image block size in original algorithm,firstly,a method is proposed to adaptively select the size of sample image patch to construct an adaptive group structure.Secondly,singular value decomposition is conducted in groups to obtain an adaptive learning dictionary of the image patch group,and the Split Bregman Iteration algorithm is used to solve the objective cost function.Finally,the adaptive dictionary and the sparse coding coefficient of each group are updated by adjusting the number of image patches and iterations in the group to get a better restoration effect.The experimental results show that this method not only improves the peak signal to noise ratio and feature similarity index of image,but also improves the repair efficiency.