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Chia-Hung Yeh^1,2, Chen Lo¹, Cheng-Han He¹

¹Department of Electrical Engineering, National Taiwan Normal University, Taipei 10610, Taiwan.

Sensors (Basel, Switzerland)

|May 11, 2024

概括

这项研究引入了一种基于波纹的多分支图像解像网络 (MBWDN),以有效地删除moiré模式. 该方法利用波纹分解和专业网络来实现优质的图像质量恢复.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
信号处理信号处理

背景情况:

莫伊尔图案是由摄像头传感器和显示器之间的别名而产生的,降低了图像质量.
图像复制对于恢复受影响图像的纹理和颜色精度至关重要.

研究的目的:

提出一个新的基于波纹的多分支图像解像网络 (MBWDN),以有效地去除moiré模式.
通过解决纹理和色彩恢复挑战来提高图像质量.

主要方法:

波段分解将moiré图像分成子频段图像 (低频和高频).
一个移除moiré网络 (MRN) 处理低频组件,保持光滑的区域.
一个细节增强的莫雷去除网络 (DMRN) 处理高频模式并增强细节.

主要成果:

MBWDN有效地去除了moiré图案,同时保留了图像的细节和结构.
波段分解和专用网络导致了令人印象深刻的莫雷去除效应.
定量和定性实验表明,与最先进的方法相比,性能优越.

结论:

拟议的MBWDN为图像拆解提供了一个强大的解决方案.
基于波形的方法与多网络策略相结合,对于移除moiré模式非常有效.
该方法在图像质量恢复方面取得了显著的改进.

关键词:

深度学习是一种深度学习.图像恢复图像恢复图像恢复莫伊尔格局的模式波形变换波形变换波形变换.

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