在动态场景中通过高低频分解进行无鬼HDR成像
Xiang Zhang1, Genggeng Chen1, Fan Zhang1
1College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究介绍了HL-HDR,这是一种用于在动态场景中无幽灵高动态范围 (HDR) 图像重建的新型网络. 它有效地平衡高频和低频信息,改善细节恢复和减少计算负载.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 在动态场景中高动态范围 (HDR) 图像生成具有挑战性.
- 变压器显示出对移动处理的承诺,但与高频细节和复杂性作斗争.
- 现有的方法往往会损害结构细节的恢复.
研究的目的:
- 为动态场景开发一个无幽灵的HDR重建网络.
- 解决变压器和CNN在捕获高频细节和管理计算复杂性的局限性.
- 通过有效处理高频和低频组件来提高HDR图像的质量.
主要方法:
- 拟议的HL-HDR网络,将特征分解为高频和低频组件.
- 引入了频率调整模块 (FAM),用于移动捕捉和细节精细化.
- 使用频率分解处理块 (FDPB) 来融合频率信息以进行精确的重建.
主要成果:
- HL-HDR有效地克服了现有的变压器和基于CNN的方法的局限性.
- 该网络在捕捉大规模运动和细化局部细节方面表现出卓越的性能.
- 在五个公共HDR数据集上取得了最先进的结果.
结论:
- 在动态场景中,HL-HDR为无幽灵的HDR重建提供了有效的解决方案.
- 提出的频率分解方法提高了细节恢复,减少了计算复杂性.
- 在动态环境中,HL-HDR代表了HDR成像技术的重大进步.
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