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At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
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相关实验视频

Updated: May 10, 2025

A Human Cerebral Organoid Model of Neural Cell Transplantation
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局部二进制模式循环生成对抗网络转移:将图像风格从白天转变为夜晚

Abeer Almohamade1,2, Salma Kammoun1, Fawaz Alsolami1

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Journal of imaging
|April 25, 2025
PubMed
概括
此摘要是机器生成的。

LBP-CycleGAN通过使用局部二进制模式 (LBP) 来实现更清晰的纹理来增强夜间图像翻译. 没有自我注意的模型实现了优越的质量,并降低了自动驾驶等应用程序的计算成本.

关键词:
当地的二进制模式 (LBP)循环生成对抗网络 (CycleGAN) 是一个循环生成对抗网络.转换图像风格 转换图像风格不配对的图像对图像翻译.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 图像对图像的翻译对于自动驾驶和监控至关重要.
  • 现有的CycleGAN模型面临着纹理损失和高计算需求等挑战.

研究的目的:

  • 开发一个改进的CycleGAN模型,用于日夜图像翻译.
  • 解决现有方法的纹理损失和计算低效率的问题.

主要方法:

  • 引入了LBP-CycleGAN,使用本地二进制模式 (LBP) 来提取纹理细节.
  • 利用基于LBP的单通道输入来改善夜间图像生成.
  • 评估了三种变化:LBP-CycleGAN与自我注意 (完全,仅有歧视者) 和没有自我注意.

主要成果:

  • 没有自我注意的LBP-CycleGAN模型与其他变体相比,产生了优越的纹理质量.
  • 这种模型显著减少了培训时间和计算开销.
  • 在夜间纹理中表现出增强的度和一致性.

结论:

  • LBP-CycleGAN提供了一种高效的解决方案,用于高保真度的夜间图像翻译.
  • 简化的模型 (没有自我注意) 提供了最佳的性能.
  • 这一进步有利于现实世界的应用,例如自动驾驶和低光视觉系统.