L1范数和分裂Bregman的遥感影像变分融合模型Remote sensing image variational fusion model based on L1 norm and split Bregman
侯昕廷;秦前清;孙涛;宋博;付志涛;
摘要(Abstract)：
目前,一些基于变分的Pan-sharpening方法是通过梯度下降法极小化能量泛函来实现融合,但梯度下降法在靠近极小值时收敛速度会减慢。若变分模型中包含有L1范数的不可微项时,梯度下降法存在鲁棒性不高、计算复杂的问题。该文根据L1范数能保持图像的几何纹理、分裂Bregman对含有L1范数的泛函收敛速度快的特点,在已有的变分模型基础上,将L1范数加入到模型中,构建能量泛函代价函数,并通过分裂Bregman迭代极小化能量泛函。在Worldview-2数据集上的融合结果表明,该方法可以生成同时具有高光谱和高空间分辨率的图像。
关键词(KeyWords)： L1范数;分裂Bregman;Pan-sharpening;能量泛函
基金项目(Foundation): 国家自然科学基金项目(41171450)
作者(Authors): 侯昕廷;秦前清;孙涛;宋博;付志涛;
DOI: 10.16251/j.cnki.1009-2307.2018.01.003
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