基于色彩聚类的图像修复

现有图像修复方式普遍存在结构错乱和修复区域边缘模糊的问题,这是由于修复过程中已知区域的结构信息未能得到充分利用,针对这一问题,本文提出了一种具有编解码结构的基于色彩聚类的图像修复算法,算法采用渐进式图像修复网络结构,将图像经过色彩聚类的结果作为输入,聚类算法处理后的图像更好保留了输入图像的结构特征,因此在后续图像纹理恢复过程中结构信息可以得到充分利用;同时,为进一步减少网络的计算开销,引入交叉注意力机制,从水平和垂直两个维度建模图像全局依赖性.实验结果表明,本文提出的图像修复算法可以有效缓解图像修复区域边缘模糊的现象,与几种主流图像修复算法相比,我们提出的图像修复算法可以在缺失区域较大的情况下产生更加真实的输出结果.

Image Restoration Based on Color Clustering

Existing image restoration methods generally suffer from structural misalignment and blurred edges of the restored area, which is due to the under-utilization of structural information in known areas during image restoration. To this end, a color clustering-based image restoration algorithm with an encoder-decoder structure is proposed in this study. The algorithm uses a progressive image restoration network structure, taking the results of the images after color clustering as input, and the images processed by the clustering algorithm better preserve the structural information. Therefore, the structural information can be fully utilized in the subsequent image texture restoration process. Meanwhile, to further reduce the computational overhead of the network, a cross-attention mechanism is introduced to model the global dependence of images from both horizontal and vertical dimensions. The experimental results show that the image restoration algorithm proposed in this study can effectively alleviate the blurring of the edges in the restored areas, and compared with several mainstream image restoration algorithms, the proposed image restoration algorithm can produce more realistic output results in the case of large missing areas.