BSCB模型在传输过程中引入Laplace算子时采用的点是某一像素周围4个邻点,对像素的表示会有局限性,进而造成修复后边缘模糊的现象。为优化这一问题,提出一种基于粗糙数据推理的改进BSCB算法,利用粗糙数据推理空间制定与某一像素相关联的采取规则以期挖掘像素之间的近似关系、衍生关系及拓展关系,选取与某一像素相关性最大的点,从而避免像素表示的局部性问题。实验结果表明,与经典的BSCB算法相比,改进后的算法在传输过程中采取的点更能体现图像结构,可获得较好的视觉效果,峰值信噪比也从数据层面证实修复效果的改善。

The Laplace operator introduced in the BSCB model during the transmission process uses four adjacent points around a certain pixel, limiting the pixel representation and then resulting in blurred edges after restoration. In this study, an improved BSCB (Bertalmio, Sapiro, Caselles, Ballester) algorithm is proposed based on rough data deduction to optimize this problem. The improved BSCB algorithm uses the rough data deduction space to formulate rules related to a certain pixel for mining the approximation, derivation, and expansion relations between pixels and adopting points that exhibit the greatest correlation with a certain pixel, avoiding the locality of pixel representation. The experimental results denote that the points adopted during the transmission process of the improved BSCB algorithm can better reflect the image structure, and the proposed algorithm can obtain a better visual effect when compared with the classical BSCB algorithm. The peak signal-to-noise ratio also confirms the improvement of the restoration effect based on the data level.