针对现有的样本块修复算法置信度值会迅速衰减至0,填充次序不稳定以及易产生误匹配等不足,提出结合梯度特征与色彩一致性的图像修复算法.为获得更为稳定的填充次序,一方面引入表征图像结构和纹理变化特征的平均梯度来计算优先权,以保证优先填充结构部分,并适度延伸纹理信息;另一方面提出基于S型函数的置信度更新准则以抑制置信度项的快速衰减;同时比较匹配块与待修复块的色彩一致性,将其与颜色信息相结合寻找最佳匹配块,以减轻误匹配现象.实验结果表明:本文算法较对比算法在峰值信噪比上至少提高0.82dB,证明了本文算法的有效性.结果也显示:提出的算法获得了更加稳定的修复次序,能有效减少误匹配率,使得修复后图像更加满足人眼的视觉需求.%In existing exemplar-based algorithms, the confidence value tends to reach zero rapidly, filling order is unstable, and mismatch can easily occur easily.To address these problems, an image inpainting algorithm using gradient features and color consistency was proposed.To obtain a more stable filling order, mean gradient was introduced to represent the change characteristics of an image structure and texture.Mean gradient was also applied to calculate the priority to ensure that the structure was preferentially populated and the texture information was appropriately extended.A confidence update term based on an S-shaped function was also proposed to avoid rapid decay of the confidence term.Color consistency between the candidate patch and the target patch was also combined with color information to identify the most similar patch, and to reduce the false matching rate.Experimental results demonstrate that the peak signal-to-noise ratio of the proposed algorithm is at least0.82 dB higher than that of existing algorithms, which indicates the validity of the proposed method.The results also show that the proposed algorithm can make the inpainting order more stable and canreduce the error matching rate, which brings the repaired images more in line with human visual requirements.
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