一个整合注意力机制和生成对抗网络的模型,用于图像风格传输
Miaomiao Fu1, Yixing Liu1, Rongrong Ma1
1School of Information Technology, Luoyang Normal University, Luoyang, China.
PeerJ. Computer science
|September 24, 2024
概括
本研究介绍了一种新的图像风格传输模型,使用循环一致的网络和注意力机制来有效处理远距离的像素依赖. 实验表明,这种方法显著提高了风格传输质量,经过感知研究验证.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 图像风格转移对于图像重建和纹理合成等任务至关重要.
- 现有的神经网络模型在与风格传输固有的远距离像素依赖性作斗争.
研究的目的:
- 为图像风格传输开发一个生成模型,有效地解决长距离像素依赖.
- 增强模型感知和利用相关风格和内容特征的能力,同时抑制不相关的特征.
主要方法:
- 构建了一个整合循环一致网络和注意力机制的生成模型.
- 循环一致的机制促进了输入和输出图像之间的不匹配转换.
- 注意力机制改善了对远距离像素依赖性的感知,并抑制了非目标风格信息.
主要成果:
- 在monet2photo数据集上进行了实验.
- 拟议的模型在亚马逊机械土耳其 (AMT) 的感知研究中实现了45%的误判率.
- 这个比率表明了高水平的感知质量和成功的风格转移.
结论:
- 循环一致的网络模型与注意力机制相结合,在图像风格传输方面表现出显著的优势.
- 该模型有效地处理内容和风格特征之间的复杂关系.
- 这种方法为需要复杂的图像处理的先进计算机视觉应用提供了有希望的解决方案.
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