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Masking and Demasking Agents

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Published on: April 21, 2023

合成文档图像具有多种影子,用于深度影子清除网络.

Yuhi Matsuo¹, Yoshimitsu Aoki¹

¹Department of Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Kanagawa, Japan.

Sensors (Basel, Switzerland)

|January 26, 2024

概括

本研究介绍了SynDocDS数据集和双阴影融合网络 (DSFN),以改进文档阴影去除. 在SynDocDS上的培训提高了性能,提高了PSNR和SSIM等指标,以更好地数字化文档应用.

科学领域:

计算机视觉计算机视觉
数字图像处理数字图像处理
机器学习机器学习

背景情况:

删除文档影子对于数字化文档至关重要.
现有的方法在有限的多样化数据集和合成数据的限制下扎.
综合数据集往往缺乏文档多样性和各种照明条件.

研究的目的:

介绍一个大规模,多样化的合成文档与多种影子 (SynDocDS) 数据集.
提出一个双影子融合网络 (DSFN) 进行强大的文件影子去除.
通过使用各种合成数据,提高深度影子清除网络性能.

主要方法:

开发了SynDocDS数据集,使用基于物理的照明模型进行多种影子染.
提出了具有高全球色彩理解能力的双阴影融合网络 (DSFN).
在SynDocDS和公共数据集 (OSR,Kligler,Jung) 上训练和评估模型.

主要成果:

在SynDocDS的培训中,平均PSNR从23.00 dB提高到25.70 dB,SSIM从0.959提高到0.971.
拟议的DSFN在多个指标 (PSNR,SSIM) 上优于现有网络.
在光学字符识别 (OCR) 性能中显著改进. 影子后去除.

关键词:

深度神经网络是一个神经网络.文件图像文件图像文件图像影子去除影子去除

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结论:

通过SynDocDS数据集,可以培训更强大,更高性能的影子清除网络.
DSFN有效地处理文档特定的颜色特征,以获得更优质的影子清除.
拟议的方法显著提高了文档图像数字化质量和可用性.