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A novel wavelet denoising method used for droplet volume detection in the microfluidic system

机译:一种用于微流系统中液滴体积检测的小波去噪新方法

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Microdroplet is found increasing use in the production of nanoparticles, chemical reactions and drug research and development. However, only some simple theory of droplet information is studied, automate droplet is the direction in the future. Size of droplet is detected in order to control the flow of the microfluid in automation droplet system. Image collected by image acquisition system has severe noise and denoising is the most important step. This paper describes a new wavelet threshold function to improve the wavelet threshold denoising. This approach successfully denoises the noise in the image. Compared with traditional wavelet threshold denoising, this approach has enormous improvements in objective evaluation of denoising such as Peak Signal Noise Ration (PSNR). With the demand of constant accurate flow in microfluidic system, the approach to measure droplet is in urgent need. The obtained results are in good agreement with the demand. The method is used to detect the radius of droplets in the microfluidic system. If dust or spot images are directly processed with noisy bring out error result even not able to be detected. Better results are achieved after processed by the approach in this paper. That boots application of microfluidic system in field of pharmacy, microchemistry, biochemistry and bioanalysis.
机译:发现微滴在纳米颗粒的生产,化学反应和药物研究与开发中的使用越来越多。然而,仅研究液滴信息的一些简单理论,使液滴自动化是未来的方向。检测液滴的大小以便控制自动化液滴系统中微流体的流动。图像采集系统采集的图像噪声严重,去噪是最重要的一步。本文介绍了一种新的小波阈值函数,以改善小波阈值降噪。这种方法成功地消除了图像中的噪声。与传统的小波阈值去噪相比,这种方法在去噪的客观评估(例如峰值信噪比(PSNR))方面有巨大的进步。随着微流体系统中恒定恒定流量的需求,迫切需要测量液滴的方法。得到的结果与需求吻合良好。该方法用于检测微流体系统中液滴的半径。如果直接处理带有噪点的灰尘或斑点图像,则会导致错误结果,甚至无法检测到。经本文方法处理后,可获得更好的结果。引导了微流体系统在药学,微化学,生物化学和生物分析领域的应用。

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