基于多视场分析的 L0 梯度约束模型提高微观成像质量。

Microscopic imaging quality improvement through L0 gradient constraint model based on multi-fields of view analysis.

机构信息

Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou Dianzi University, Hangzhou 310018, PR China; School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, PR China.

Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou Dianzi University, Hangzhou 310018, PR China.

出版信息

Micron. 2019 Sep;124:102709. doi: 10.1016/j.micron.2019.102709. Epub 2019 Jun 29.

DOI:10.1016/j.micron.2019.102709

PMID:31280005

Abstract

The degradation of optical microscopic imaging is space-variant, and how to fast restore optical degraded image remains a special problem. Based on point spread function (PSF) estimation under each field of view (FOV), a L0 gradient-constrained image restoration method is proposed to solve optical degradation in microscopic imaging. Firstly, the whole scene is segmented into several different regions according to different FOV. The PSFs for each region are estimated from modulation transfer function (MTF) measured in advance. Secondly, a penalty function is designed using L0 gradient constraint to deblur the degraded images of each sub-FOV. Finally, a weighted stitching approach is used to stitch the restored images of multiple FOV (m-FOV). Experimental results indicate that the m-FOV analysis could well solve the problem of space-variant degradation. Compared with the other methods, both subjective and objective evaluation results prove that the L0 norm idea could rapidly and effectively restore the degraded image. The approach could be well applied to a real product.

摘要

光学显微成像的退化具有空间变异性，如何快速恢复光退化图像仍然是一个特殊的问题。基于每个视场（FOV）下的点扩散函数（PSF）估计，提出了一种 L0 梯度约束图像恢复方法，以解决微观成像中的光退化问题。首先，根据不同的 FOV 将整个场景分割成几个不同的区域。从预先测量的调制传递函数（MTF）中估计每个区域的 PSF。其次，设计了一个使用 L0 梯度约束的惩罚函数来对每个子 FOV 的降质图像进行去模糊。最后，使用加权拼接方法将多个 FOV（m-FOV）的恢复图像拼接起来。实验结果表明，m-FOV 分析可以很好地解决空间变异性退化的问题。与其他方法相比，主观和客观评估结果都证明了 L0 范数思想可以快速有效地恢复降质图像。该方法可以很好地应用于实际产品。