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相关概念视频

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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相关实验视频

Updated: Jun 13, 2025

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图像与简单的混合CNN-变压器网络的图像协调.

Guanlin Li1, Bin Zhao2, Xuelong Li2

  • 1School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, Shannxi, China; School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, 710072, Shannxi, China.

Neural networks : the official journal of the International Neural Network Society
|September 11, 2024
PubMed
概括

本研究介绍了一种新的简单混合CNN转换器网络 (SHT-Net),用于图像协调. SHT-Net有效地模拟全球-本地像素照明,增强图像细节和颜色一致性.

关键词:
混合CNN-变压器结构结构.图像协调与图像协调模块化的卷积.平行注意力注意力.

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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
  • 图像处理 图像处理

背景情况:

  • 图像协调旨在使前景照明与复合图像中的背景照明相匹配.
  • 目前的方法很难捕捉必要的全球-本地像素照明依赖性,以获得清晰,颜色一致的结果.

研究的目的:

  • 开发一个新的网络,SHT-Net,能够建立全球-本地像素照明依赖性,以改善图像协调.
  • 为了生成具有细粒度细节和一致的颜色的照片现实性和的图像.

主要方法:

  • 设计了一个对称的等级架构,命名为简单的混合CNN转换器网络 (SHT-Net).
  • 整合了两个新的变压器块:用于多尺度特征捕获的规模感知门区块和用于全球-本地照明建模的并行注意区块.
  • 使用高效的前网络来改进功能以实现现实的输出.

主要成果:

  • 在标准图像协调基准上取得了有希望的定量和质量结果.
  • 证明了SHT-Net在捕获多尺度特征和建模像素照明关系方面的有效性.
  • 创建了具有增强细节和色彩一致性的协调图像.

结论:

  • 通过有效地建模全球-本地照明依赖性,SHT-Net在图像协调方面取得了重大进展.
  • 拟议的架构和注意力机制有助于生成高质量,摄影现实性和的图像.
  • 该方法为复合图像照明转移的挑战提供了强大的解决方案.