针对当前较多图像修复算法依靠固定大小样本块来搜寻最优匹配块，忽略了样本块的结构信息，使修复图像出现间断现象以及振铃现象等不足，利用样本块与其邻域块的近似度，设计了一种采用结构信息约束法则与 匹配模型的图像修复算法。将图像的信息熵特征引入到待修复块的优先权计算过程中，获取优先修复块。通过样本块与其邻域块的近似度构造结构信息度量模型，对样本块的结构信息进行度量，并根据度量值建立结构信息 约束法则，实现样本块大小的自适应调整。最后，利用图像的色彩及灰度特征构造匹配模型，利用调整后的样本块大小在已知区域中寻找最优匹配块，从而对待修复块进行修复。实验结果显示，所提算法得到的修复图像具 备较好的纹理连续性，不存在信息间断现象，对应的结构相似度较高。

In view of the fact that most image restoration algorithms rely on fixed size sample blocks to search for the best matching blocks, ignoring the structure information of the sample blocks, resulting in the discontinuity and ringing phenomenon of the repaired images. Based on the approximation between the sample block and its neighborhood block, this paper designs an image restoration algorithm by using the structure information constraint rule and matching model. The information entropy feature of the image is introduced into the priority calculation of the blocks to be repaired, and the priority repaired blocks are obtained. Structural information measurement model is constructed by the number of known pixels in the sample block to measure the structural information of the sample block. Constraint rules of structural information are established according to the measured values to adaptively adjust the size of the sample block. The matching model is constructed by using the color and gray features of the image, in order to find the best matching block in the known region after adjusting the size of the sample block, and repair the block needing repairing. The experimental results show that the restored image obtained by the proposed algorithm has high structural similarity, good texture continuity, and no information discontinuity.