Abstract: The resolution image reconstruction algorithm of structured light microscopic imaging system has some problems such as ring effect, poor resistance to noise. This paper proposes a novel algorithm based on L1 norm and total variation regularization. Firstly, the design principle and limitations of the traditional algorithm are analyzed. Secondly, the principle of L1/TV and how to using L1 norm to constrain the resolution for reconstructing images are discussed. And then it uses total variation regularization to overcome the pathology while reconstructing the image and protect the edges of the image. The experimental results show that：L1/TV algorithm outperforms the traditional ones because it has a stronger resistance for noises and a higher resolution for the reconstructed images.

Key words: total variation, regularization, super resolution, L1 norm, reconstruction

摘要： 针对结构化照明显微成像系统的超分辨图像重构算法存在边界振铃效应、噪声免疫性差的问题，提出了一种基于L1范数的全变分正则化超分辨图像重构算法（简称L1/TV重构算法）。从结构化显微成像模型入手，分析了传统算法的设计原理和局限性；论述了L1/TV重构算法的原理，采用L1范数对重构图像保真度进行约束，并利用全变分正则化有效克服了重构过程的病态性，保护了重构图像边缘。对比研究传统重构算法和L1/TV重构算法的性能。实验结果表明：L1/TV重构算法具有更强的抗噪声干扰能力，重构图像空间分辨率更高。

关键词: 全变分, 正则化, 超分辨, L1范数, 重构