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相关概念视频

Upsampling01:22

Upsampling

581
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
581

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相关实验视频

Updated: Jan 15, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

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Published on: June 27, 2025

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实时高分辨率照片增强:拉普拉西亚金字塔网络

Feng Zhang, Haoyou Deng, Zhiqiang Li

    IEEE transactions on pattern analysis and machine intelligence
    |October 15, 2025
    PubMed
    概括
    此摘要是机器生成的。

    LLF-LUT++使用新的金字塔网络来增强照片,平衡高分辨率图像的性能和速度. 这种高效的方法在基准数据集上取得了卓越的结果.

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    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:

    • 当前的照片增强方法面临性能和计算效率之间的权衡.
    • 高性能模型通常对边缘设备来说过于资源密集.
    • 专注于效率的模型往往没有足够的质量来实现实际使用.

    研究的目的:

    • 介绍LLF-LUT++,一个新的金字塔网络,用于高效和高性能的照片增强.
    • 通过整合全球和本地图像处理技术来解决现有方法的局限性.
    • 在资源有限的设备上实现高分辨率图像的快速处理.

    主要方法:

    • 利用封闭形式的拉普拉斯金字塔分解和重建来整合全球和本地运营商.
    • 采用了适应图像的3D查看表 (LUT) 以基于下采样图像特征的全球音调增强.
    • 包含空间频率变压器重量预测器和局部拉普拉斯波器,用于自适应细节的精细化.

    主要成果:

    • 在HDR+数据集上实现了2.64dB的峰值信号噪声比 (PSNR) 改进.
    • 在单个GPU上在13毫秒内处理4K分辨率图像,显示了显著的速度改进.
    • 在对两个基准数据集的广泛实验中超越了最先进的方法.

    结论:

    • LLF-LUT++成功地将增强性能与计算效率相平衡.
    • 拟议的网络架构和变压器模型能够快速,高质量的图像增强.
    • 该方法为需要快速有效的照片增强的现实应用提供了可行的解决方案.