图像去噪问题的非线性各向异性扩散方法:挑战与未来研究机会

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS
Array Pub Date : 2023-03-01 DOI:10.1016/j.array.2022.100265
Baraka Maiseli
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引用次数: 0

摘要

非线性各向异性扩散由于其能够同时去除噪声和保留语义图像特征而引起了人们的广泛关注。这种能力有利于多种图像处理和计算机视觉应用,包括医学和科学图像中包含关键特征(纹理、边缘和轮廓)的噪声去除。尽管基于非线性各向异性扩散的方法具有良好的性能,但其实际局限性在文献中很少讨论。我们的工作揭示了这些局限性,试图创造未来的研究机会。此外,我们还提出了一种扩散驱动的方法,与经典方法相比,该方法产生了更好的结果,包括流行的Perona–Malik公式。所提出的方法嵌入了一个内核,该内核正确地引导图像区域之间的扩散过程。实验结果表明,我们的内核有助于有效地去除噪声,并确保保留重要的图像特征。我们提供了潜在的研究问题,以进一步扩展当前的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonlinear anisotropic diffusion methods for image denoising problems: Challenges and future research opportunities

Nonlinear anisotropic diffusion has attracted a great deal of attention for its ability to simultaneously remove noise and preserve semantic image features. This ability favors several image processing and computer vision applications, including noise removal in medical and scientific images that contain critical features (textures, edges, and contours). Despite their promising performance, methods based on nonlinear anisotropic diffusion suffer from practical limitations that have been lightly discussed in the literature. Our work surfaces these limitations as an attempt to create future research opportunities. In addition, we have proposed a diffusion-driven method that generates superior results compared with classical methods, including the popular Perona–Malik formulation. The proposed method embeds a kernel that properly guides the diffusion process across image regions. Experimental results show that our kernel encourages effective noise removal and ensures preservation of significant image features. We have provided potential research problems to further expand the current results.

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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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