参考意识图像协调与参考意识图像协调
Han Guo1, Hongling Gu1, Bolun Zheng1
1School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
概括
本研究介绍了参考意识图像协调网络 (RANet),通过使用参考模块来改善图像协调,以实现全球风格和本地区域对齐,即使没有类似的内容.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 图像协调对于逼真的图像构成至关重要.
- 这是一个错误的问题,因为缺乏明确的调整标准.
- 当前的方法在前景和背景缺乏类似的内容时,就会遇到困难.
研究的目的:
- 开发一个新的网络,以实现强大的图像协调.
- 在前景和背景内容不相似的场景中解决当前方法的局限性.
- 为了在协调图像中提高全球风格和本地视觉一致性.
主要方法:
- 拟议的参考意识图像协调网络 (RANet).
- 引入了一种风格意识的参考模块,使用扩散模型进行全球风格对齐.
- 设计了一个区域意识的参考模块,用于适应性本地区域利用的软面罩.
主要成果:
- 兰网实现了有效的图像协调.
- 在iHarmony4数据集和现实世界移动数据集上表现出更好的性能.
- 验证了全球风格和当地地区意识的有效性.
结论:
- 拟议的RANet有效地协调图像,即使没有类似的前景背景内容.
- 风格意识和区域意识模块提高了协调质量.
- 该方法在图像组合和编辑方面提供了显著的进步.
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