摘要
在L1范数图像超分辨率重建框架下,引入参数自适应估计,该方法对模型误差表现出良好的稳健性并且可以加速收敛。结合差分图像统计特性和概率先验模型,解释了L1范数形式的双边全变差正则项概念,利用Kullback-Leibler距离证明了该正则项的优越性,并分析了混合先验模型在超分辨率重建中应用的可行性等问题。
A method for automatically estimating parameters is applied to the L1-norm-based image super-resolution reconstruction framework. The approach can get a stable result with model errors and can improve convergence. A novel explanation of L1-norm-based bilateral total variation（BTV） term is presented according to pixel differences statistics, combined with probability prior model. Superiority of BTV is validated using Kullback-Leibler distance. A theoretical analysis of the feasibility and the problems of super-resolution reconstruction are given using mixture prior distribution.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第24期203-205,共3页
Computer Engineering
基金
国家教育部新世纪优秀人才支持计划基金资助项目(2006-1801)
关键词
超分辨率
正则化
差分图像
super-resolution
regularization
pixel difference image
