针对光学相机成像分辨率低、噪声干扰严重等问题，本文提出一种能有效去噪的高精度超分辨方法—基于快速l1-范数稀疏表示和二阶广义全变分(TGV)的超分辨方法。首先利用各向异性扩散张量(ADT)作为边缘高频信息，通过快速l1-范数稀疏表示方法建立LR图像和相对应的高频信息ADT的字典集；其次将字典学习到的ADT边缘信息和TGV模型组合成新的规则项；最后利用新的规则项建立超分辨代价函数，并利用图像增强后处理方法对整幅图像进行优化。结果表明：算法对仿真数据和SO12233靶标数据具有较高的可行性和鲁棒性，能有效去除噪声等异常点，获得高质量清晰图像，同时与其他经典算法相比，所提算法超分辨的峰值信噪比和结构相似度均有所增大。
基于快速l1-范数稀疏表示和TGV的超分辨算法研究
作者单位信息
通信作者： hitshaoshuomu@163.com
出版日期：2019年11月15日
摘要
参考文献
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基金项目：
国家自然科学基金资助项目(61877053)；浙江省教育厅一般科研资助项目(Y201840087)
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引用本文：
穆绍硕, 张解放. 基于快速l1-范数稀疏表示和TGV的超分辨算法研究[J]. 光电工程, 2019, 46(11): 180499.
