With the advantages of low power consumption, flexibility, and high readability against bright ambiance, electrophoretic displays (EPDs) have wide application prospects in the fields of education, smart supermarkets, Internet of Things, smart homes, wearable devices, etc. EPDs suffer from a severe history dependence during grayscale switching, which results in annoying ghost images. However, currently, it is difficult to distinguish diverse types of ghost images automatically; thus, lookup-tables (LUTs) for multi-grayscale waveform design cannot be effectively generated but require cumbersome manual adjustment. In this article, we proposed to adopt a convolutional neural network (CNN) to automatically recognize ghost images, based on which, LUTs that could suppress ghost images and achieve accurate grayscales were automatically generated for waveform design. Moreover, the workforce for manual adjustment was significantly saved. The results suggest that the CNN is a powerful tool for EPDs to achieve better image quality, as well as less manual cost.

中文翻译：

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用于电泳显示器鬼像识别和波形设计的卷积神经网络

电泳显示器（EPD）具有低功耗、灵活、明亮环境下可读性高等优点，在教育、智能超市、物联网、智能家居、可穿戴设备等领域具有广泛的应用前景。灰度切换期间严重的历史依赖性，这会导致令人讨厌的重影图像。但是，目前很难自动区分多种类型的重影；因此，无法有效生成用于多灰度波形设计的查找表 (LUT)，而是需要繁琐的手动调整。在本文中，我们提出采用卷积神经网络（CNN）来自动识别鬼影，基于此，自动生成可以抑制重影并实现准确灰度的 LUT，用于波形设计。此外，大大节省了手动调整的劳动力。结果表明，CNN 是 EPD 实现更好图像质量以及减少人工成本的强大工具。