关于数字图像否定方法的研究概述
Jing Mao1, Lianming Sun2, Jie Chen3
1Graduate School of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本综述比较了传统和深度学习的图像破坏方法. 它强调了深度神经网络在消除噪音方面的有效性,同时保留了图像细节,为未来的研究提供了洞察力.
科学领域:
- 图像处理 图像处理
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 由于采集和传输,图像噪声会降低质量.
- 有效的图像消除对后续任务,如细分和识别至关重要.
- 两维振幅图像无处不在,这使得否认研究成为优先事项.
研究的目的:
- 为传统和基于深度学习的图像染方法提供全面的概述和比较.
- 归类和总结现有的消毒方法.
- 为了确定未来的研究挑战和方向在图像denoising.
主要方法:
- 复习和分类的经典传统的除技术 (例如,BM3D).
- 分析基于深度神经网络的图像拒绝框架.
- 使用公开拒绝的数据集进行定量和定性比较.
主要成果:
- 深度学习方法在图像消除方面显示出显著的前景.
- 像BM3D这样的传统方法有效地消除噪音,同时保留细节.
- 对比分析提供了对不同方法的优点和弱点的见解.
结论:
- 深度学习是图像消除的关键未来方向.
- 了解算法差异有助于选择和创新.
- 这一综述为该领域的研究人员提供了有价值的观点.
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