目的 针对基于样本块的Criminisi图像修复算法易发生置信项迅速下降趋于零，使优先权计算公式失效，导致修复顺序错乱造成的修复效果失真问题，以及在搜索匹配块时存在的搜索范围过大，效率过低，易出现匹配到不符合视觉效果的纹理块问题，提出一种基于优先权改进和块划分的图像修复算法。方法 首先重新定义优先权中的置信项，用样本块中的棋盘距离替代原计算公式，保证优先权一直发挥作用，从而减少因修复顺序不合理造成的错误匹配；其次根据图像纹理信息将其自适应划分为不同大小的图像块，使待修复样本块只在具有相似特征的图像块区域内搜索匹配。结果 实验结果表明，新定义的优先权，保证了修复算法的正常进行，改善了修复图像的视觉效果；由图像自适应块划分引导匹配过程，可使匹配在更少的候选块中进行，提高了算法速度。将本文方法与3种全局搜索匹配方法和1种局部搜索匹配方法进行修复结果对比分析，本文方法的修复结果视觉完整性较好，而且修复时间小于其中3种算法。结论 通过改进Criminisi算法优先权中的置信项，避免因其趋于零导致的修复顺序错乱造成的错误累积情况的发生；同时通过改进待修复匹配块的搜索范围，对整幅图像进行自适应块划分，使搜索只在相似块中进行，不仅减少了时间，而且提高了匹配的准确性。本文方法对于自然图像中大面积目标物体移除方面有较好的应用，可获得较满意的修复效果。

Objective The exemplar-based image inpainting method known as the Criminisi algorithm occasionally exhibits poor inpainting effect because the confidence term easily decreases to zero,which makes the priority invalid and causes disorders in the inpainting sequence.Excessive scope,low efficiency,and nonvisual texture-matching problem also occur when searching matching patches.To solve the aforementioned problems,an image completion algorithm based on redefined priority and image division is proposed in this study. Method First,the confidence term in the priority is redefined,and the chessboard distance in the exemplar patch is used to replace the original calculation formula.Accordingly,the priority is validated,and the matching error caused by unreasonable inpainting order is reduced.Second,the image is divided into blocks with different sizes according to image texture information,such that the exemplar patches to be inpaintied search only the image block region with similar features.Result Experimental results show that the newly defined priority can guarantee the completion of the algorithm and improve the visual effect of the inpainted image.The algorithm is fast because only a few ambiguous matching candidates are searched under the guidance of image division.The completion result analysis of our method is compared with the results of the analysis based on other methods.Subjectively,our method can maintain visual connectivity.Objectively,the time consumed is less than those consumed by most of the other methods.Conclusion The accumulation of errors caused by the disorder of inpainting sequence is avoided by redefining the confidence term in the priority in the Criminisi algorithm.Reduced algorithm time and improved matching accuracy are also achieved by improving the searching range of exemplar patches.The entire image is adaptively divided into different blocks,and search is conducted only in certain blocks that are similar to the destination.The method exhibits good application in object removal in natural images,and its completion effects are satisfactory.