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篇名 |
Arbitrary Style Transfer of Facial Image Based on Feed-forward Network and Its Application in Aesthetic QR Code
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並列篇名 | Arbitrary Style Transfer of Facial Image Based on Feed-forward Network and Its Application in Aesthetic QR Code |
作者 | Shanqing Zhang、Shengqi Su、Li Li、Jianfeng Lu、Ching-Chun Chang |
英文摘要 | QR code has become essential in daily-life because of the popularity of mobile devices. The visual effect of a conventional QR code is not ideal. Consequently, many good aesthetic algorithms have been proposed. However, both the decoding rate and visual effect of a QR code cannot be guaranteed simultaneously when facial image serves as the background. We propose an arbitrary style transfer of facial image based on feed-forward network as a preprocessing algorithm for an aesthetic QR code. The deep characteristics of content image and style image are unified in the same layer of convolutional neural networks in our style transfer network. Styles are changed. The result of style transfer is restricted with semantic segmentation result, color uniform regularization of facial image and repeating restriction similarity constraints. Experimental results show that both the decoding rate and visual effect of a QR code are guaranteed when our method is used in background preprocessing. |
起訖頁 | 511-522 |
關鍵詞 | Arbitrary style transfer、Feed-forward network、Facial image、Aesthetic QR code |
刊名 | 網際網路技術學刊 |
期數 | 202003 (21:2期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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