【摘要】 为解决单幅图像的超分辨重建问题,提出一种基于聚类的单帧图像超分辨率重建方法。从高分辨率样本图像中学习一个结构聚类型的高分辨率字典,利用迭代收缩算法优化目标方程,求得高分辨率图像的表示系数,使用学习到的高分辨率字典对低分辨率图像进行重构。实验结果表明,与总变分方法、软切割方法和稀疏表示方法相比,该方法的单帧图像超分辨率重建效果较好。更多还原

【Abstract】 To the question of single frame image super-resolution,implement single frame image super-resolution with the prior of training images,this paper proposes a single frame image super-resolution reconstruction method based on clustering.It builds a structural clustering based high-resolution dictionary from a set of high-resolution images,optimizes objective equation by using iterative shrinkage solution to solve the representation coefficient of high-resolution image,reconstructs low-resolution image by exploiting the learned high-resolution dictionary.Experimental results show that compared with Total Variation(TV) method,Softcuts method and Sparse Representation(SR) method,the effect of the single frame image super-resolution reconstruction of this method is better.更多还原