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相关概念视频

Upsampling01:22

Upsampling

569
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...
569
Deconvolution01:20

Deconvolution

527
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
527
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

666
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

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OPERA net Otsu驱动的性能增强的图像恢复算法

Pallvi Sharma1, Priyanka Jarial2, Bhim Sain Singla3

  • 1Department of Computer Science and Engineering, Punjabi University Patiala, Patiala, Punjab, 47002, India. pallvigautam5618@gmail.com.

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概括

这项研究介绍了一种混合图像消除技术,该技术结合了波形变换和非局部介质过. 该方法有效地消除噪音,同时保留图像细节,优于现有的方法.

关键词:
图像无效化 图像无效化非本地平均值的非本地平均值.在Otsu上设有值.软门持有 软门持有波段变换的波段变换是什么

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科学领域:

  • 数字图像处理 数字图像处理
  • 计算机视觉 计算机视觉

背景情况:

  • 现实世界的数字图像遭受各种类型的噪音 (例如,高斯式,波桑式,盐和胡式).
  • 现有的图像消除技术经常面临计算复杂性和过度平滑的挑战,导致重要的图像特征丢失.
  • 有效的降噪对于保持图像质量和结构完整性至关重要.

研究的目的:

  • 为了引入一种新的混合图像消除技术.
  • 解决现有方法的局限性,特别是计算复杂性和过度平滑.
  • 为了提高边缘保护和整体图像质量在denoising过程中.

主要方法:

  • 建议采用混合方法,将波形变换和非局部平均值 (NLM) 过结合起来.
  • 增强的Otsu值被整合到波形变换阶段中,用于初始降低噪音.
  • 随后应用NLM过,以进一步细化无色化图像并保留边缘.

主要成果:

  • 拟议的混合技术在柯达24数据集上实现了卓越的性能.
  • 量化指标显示了显著的改善:PSNR (34.86),SSIM (0.93) 和RMSE (4.61).
  • 使用FOM (0.99) 和VIF (0.59) 评分进行的进一步评估证实了该方法的有效性和优越性.

结论:

  • 混合波形-NLM无声化方法为数字图像的噪声降低提供了有效的解决方案.
  • 该技术成功地平衡了消除噪音的方法,同时保留了关键的图像结构和边缘.
  • 实验结果验证了与传统方法相比,提议的方法的增强性能和稳定性.