摘 要: 通过卷积神经网络对不同图片的内容和风格进行融合,可生成风格多样化的图片。这不但为影视制作提供了丰富的素材,更有利于图像修复和图像增强。针对这类问题,前人曾提出一些算法,但很难在时间和空间方面都达到很好的效果。这里提出一种基于TensorFlow(将复杂的数据结构传输至人工智能神经网中进行分析和处理过程的系统)的条件归一化网络来支持多风格融合及图片快速迁移,多风格可共用一个网络模型,这大大减少了算法耗时,并缓解了模型存储耗费空间大的问题,节省了计算机资源。时间上优于传统迁移算法三个数量级,空间上25种风格可共用一个模型。同时,更大程度地保留了内容图的语义特征,具有更好的视觉效果。 |
关键词: 条件归一化网络;风格融合;快速迁移;共享模型 |
中图分类号: TP312
文献标识码: A
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Picture Style Fusion and Rapid Migration |
CHEN Liang
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(School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China )
m13125397685@126.com
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Abstract: Content and style of different pictures can be merged by convolutional neural network to generate pictures with diversified styles. This not only provides rich materials for film and television production, but also facilitates image restoration and enhancement. Aiming at this kind of problem, previous researchers have proposed some algorithms, which turned out to be difficult to achieve good results in both time and space. This paper proposes a conditional normalization network based on TensorFlow (a system that transmits complex data structures to artificial intelligence neural networks for analysis and processing) to support multi-style fusion and rapid image migration. Multiple styles share a network model, which greatly reduces time-consuming algorithm and relieves large space consumption for model storage, saving computer resources. It is three orders of magnitude better in time than traditional migration algorithms, and 25 styles in space share one model. At the same time, semantic features of the content map are retained to a greater extent, and the visual effect is better. |
Keywords: conditional normalization network; style fusion; rapid migration; sharing model |