基于Hadoop的改进聚类算法在图像修复上的应用

Application of improved clustering algorithm based on Hadoop in image inpainting

针对模糊聚类算法在运算大数据量时性能差的问题,提出基于Hadoop分布式平台的改进算法进行图像修复。对于受损图像信息,首先将Canopy算法和模糊聚类相结合在Hadoop平台上进行并行化,然后进行字典训练获得修复图像。实验结果表明,该算法在均方误差和峰值信噪比上均优于改进前的图像修复算法,提高了图像修复质量并且减少了算法的运行时间,适合修复海量图像。

Aiming at the problem that the fuzzy clustering algorithm is poor in computing large data volume,an improved algorithm based on Hadoop distributed platform is proposed for image restoration. For the damaged image information,the Canopy algorithm and the fuzzy clustering are combined on the Hadoop platform for parallelization,and then the dictionary is trained to obtain the repaired image. The experimental results show that the algorithm is superior to the previous image restoration algorithm in terms of mean square error and peak signal to noise ratio,which improves the quality of image restoration and reduces the running time of the algorithm. It is suitable for repairing massive image.