解决反向问题使用扩散与代色彩 Renoising 使用扩散
Matthew C Bendel1, Saurav K Shastri1, Rizwan Ahmad2
1Dept. Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.
概括
我们引入快速代反响 (FIRE),以改善无监督成像与扩散模型的反向问题解决. 通过代地改进估计并重新调整它们以提高模型兼容性,FIRE提高了准确性和速度.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 无监督成像反向问题可以使用预训练的扩散模型来解决.
- 这需要在扩散反向过程中近似测量条件得分函数的梯度.
- 现有的近似方法表现不佳,特别是在逆流程的早期.
研究的目的:
- 开发一种新的方法来提高通过扩散模型解决成像反向问题的准确性和效率.
- 为了解决现有的得分函数近似方法的局限性.
- 引入一种新的代重新估计和重新噪声技术.
主要方法:
- 提出FIRE (Fast Iterative REnoising) 一种代方法,该方法在每个扩散步骤中重新估计和重新估计噪声数次.
- FIRE注入了有形的彩色噪声,以确保扩散模型始终接收白噪声,与其训练保持一致.
- 将FIRE嵌入到DDIM反向过程中,创建一个名为DDfire的新方法.
主要成果:
- DDfire在各种线性反向问题上展示了最先进的准确性和运行时性能.
- 在阶段检索任务中,DDfire取得了出色的结果.
- 拟议的FIRE方法显著改进了现有的得分函数近似技术.
结论:
- 通过使用扩散模型,FIRE方法为使用扩散模型解决成像反向问题的无监督解决提供了实质性的改进.
- 对于像相位检索这样的任务,DDfire提供了一个计算效率高和高度准确的解决方案.
- 开发的方法提高了预训练的扩散模型与反向问题解决需求的兼容性.
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