无幽灵的HDR成像通过潜在的低频前置和可变形的注意力对齐
概括
本研究介绍了低频感知扩散 (LF-Diff),这是一个高效的模型,用于从低动态范围 (LDR) 图像中创建高动态范围 (HDR) 图像. 在保持高质量的同时,LF-Diff显著加快了基于扩散模型的HDR重建速度.
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:
- 从多个低动态范围 (LDR) 图像中恢复无幽灵的高动态范围 (HDR) 图像是由于和和运动而具有挑战性的.
- 扩散模型 (DMs) 在HDR成像中表现有前途,但在实际应用中计算效率低下.
研究的目的:
- 开发一种高效有效的方法来实现无幽灵的HDR图像重建.
- 解决在HDR成像中扩散模型的计算低效.
主要方法:
- 提出了低频感知扩散 (LF-Diff) 模型,在一个紧的隐性空间中实现DM,并与基于回归的模型集成.
- 利用DMs创建低频先验进行重建,增强细节.
- 引入了注意引导可变形对齐模块 (ADAM) 以实现高效的LDR图像预对齐.
主要成果:
- 在无幽灵的HDR成像中,LF-Diff的性能优于最先进的方法.
- 拟议的方法比以往基于扩散模型的方法快10倍.
- 在合成和现实世界数据集上都表现出有效性.
结论:
- LF-Diff提供了一个高效和高质量的解决方案,用于无幽灵的HDR图像重建.
- 整合低频前置和高效对齐模块可以提高HDR成像中的DM性能.
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