基于自组织神经网络SOM和K-means聚类算法的图像修复

近来自然图像的修复已经成了一个热门话题.提出了一种基于K-means聚类算法的自组织神经网络(SOM),称为SOM-K.它首先利用SOM来训练每一个像素的特征向量,并把一幅图像分层.这样就能把每个破损像素分到每层,同时SOM训练后的输出也通过K-means聚类算法来聚合,分别在各个层中修复破损的像素.最后把修复好的各层溶合到一起.与单独使用SOM相比,SOM-K具有更精确的分类能力.

Image Inpainting Method Based on Self-Organizing Maps and K-means Clustering

Natural image inpainting has been a hot topic in recent year.A SOM based K-means(SOM-K) method for inpaintingis presented.Feature vectors of each pixel are first trained by a SOM neural network for dividing an image into several layers,and assign each damaged pixel to one layer,then the output of SOM are clustered by K-means clustering method,restoring these damaged pixels by the information of their respective layer.At last,these inpainted layers are fused together.Compared to SOM,SOM-K makes a more precise segmentation in most cases by dividing an image into several layers.