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。
关键词:
超分辨率,
稀疏表示,
重建方法,
聚类,
字典,
迭代
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.
Key words:
super-resolution,
sparse representation,
reconstruction method,
clustering,
dictionary,
iteration
中图分类号:
李娟娟, 李小红. 基于聚类的单帧图像超分辨率重建方法[J]. 计算机工程, 2013, 39(7): 284-287.
LI Juan-Juan, LI Xiao-Gong. Super-resolution Reconstruction Method for Single Frame Image Based on Clustering[J]. Computer Engineering, 2013, 39(7): 284-287.