【摘要】 现有基于稀疏性的图像修复算法采用固定大小的待填充块和邻域一致性约束,且在全局搜索待填充块的最优匹配块,既降低了待修复区域的结构连贯性和纹理清晰性,又增加了算法的时间复杂度.针对上述问题,根据破损区域特性和块结构稀疏度间的关系,提出基于块结构稀疏度的自适应图像修复算法.根据最大优先权值点的块结构稀疏度值,设定不同参数以自适应选取待填充块大小、邻域一致性约束权重系数和局部搜索区域大小,并通过仿真实验分析讨论了各参数选取.实验结果表明本文算法较文献算法在峰值信噪比上提高0.3dB～1.2dB,并且提高算法速度3～7倍.更多还原

【Abstract】 In the existing patch sparsity based image inpainting algorithms,the exemplar-size and neighborhood-consistence weight are fixed,and the best match patches of the patch to be filled are searched in the whole source region.However,it decreases the connectivity of structure and clearness of texture while increases the time complexity of this algorithm.To address these problems,an adaptive image inpainting algorithm is proposed based on patch structure sparsity,in the light of the relationship between the characteristics of damage region and patch structure sparsity.According to patch structure sparsity value of the point which has the maximal prority value,the size of patch to be filled,the neighborhood consistence weight and the part-search region size are adaptively confirmed through setting some parameters,then these parameters are analysed and discussed by some experiments.Experimental results show that the PSNR is increased by 0.3～1.2dB and the speed is improved by 3～7 times compared with the existing algorithms.更多还原