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Sinature Super-Resolution

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# Signature Super-Resolution

签字序列数据超分辨率重建工具库

📝 三部分:

  • ssr_method:超分重建方法,现有基于贝塞尔曲线插值的方法

  • metric:评价指标,现有dtw方法(序列不需要等长)和rmse(序列必须等长)

  • utils:笔迹序列数据,速度数据对比可视化,下采样

🔨 可用于:

  • 低报点率设备采集到的数据(lr_data),经过重建,输出超分辨率数据(sr_data)

  • 输入高报点率设备采集到的数据(hr_data),经过下采样,重建,评价超分方法

📌 签字序列数据输入/输出规范:

  • pd.DataFrame:[“X”, “Y”, “P”, “T”],其中”T”维度数据非时间间隔和时间戳,是时间递增,从0开始

## ssr_method ### 贝塞尔插值 - hr2lr2sr:输入高清数据和低清数据采样率,经过下采样(down_sample),超分重建(SR),输出超分辨率数据 - ssr_bezier:输入低清数据和高清数据采样率,经过超分重建(SR),输出超分辨率数据

📆 TODO - [ ] GAN SR

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