基于变压器的图像颜色化
Min Xu1,2
1School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China. minxu@sspu.edu.cn.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种基于变压器的深度学习方法,用于自动灰度图像颜色化. 该方法实现了自然色彩和丰富的细节,显著改善了黑白图像增强.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 灰度图像缺乏视觉信息.
- 手动调色是耗时的和主观的.
- 需要自动化方法来实现现实的图像增强.
研究的目的:
- 开发基于变压器的深度学习模型,用于自动灰度图像颜色化.
- 为了提高彩色图像的准确性和视觉质量.
- 为了利用深层架构和注意力机制来提取和预测特征.
主要方法:
- 使用基于变压器的深层架构,堆叠编码器-解码器层.
- 采用自我注意机制和上下文信息来预测色度组件 (例如,CIELAB中的ab通道).
- 整合了输入光度通道 (L通道) 与预测的色度通道,用于最终的色化.
主要成果:
- 在黑白图像彩色化方面取得了显著的性能改进.
- 生成的图像呈现出自然的色彩和丰富的细节.
- 与现有方法相比,证明了比现有方法更高的像素精度和视觉质量.
结论:
- 拟议的基于变压器的方法有效地色化灰度图像.
- 该模型的深度架构和注意力机制增强了特征提取和预测.
- 结果显示,在图像增强和修复方面的实际应用具有很高的价值.
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