Alkinani Monagi H, El-Sakka Mahmoud R
1Department of Computer Science, University of Jeddah, Asfan Road, 285, Dhahban, Jeddah, 23881 Saudi Arabia.
2Department of Computer Science, Middlesex College, Western University, 1151 Richmond Street, London, Ontario, N6A 5B7 Canada.
EURASIP J Image Video Process. 2017;2017(1):58. doi: 10.1186/s13640-017-0203-4. Epub 2017 Aug 24.
Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study.
We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time.
Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods.
Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.
在数据采集阶段，数字图像通过传感器获取，此时它们常常被噪声（一种不期望的随机信号）污染。这种噪声也可能在传输过程中或因低质量的有损图像压缩而产生。减少噪声和增强图像被认为是所有其他数字图像处理任务的核心过程。图像去噪方法性能的提升将对其他图像处理技术的结果有很大贡献。基于块的去噪方法最近已成为针对各种加性噪声水平的最先进去噪方法。在这项工作中，研究了使用最先进的基于块的去噪方法来减少加性噪声。针对各种类型的图像数据集开展了本研究。
我们首先解释数字图像中的噪声类型，并讨论各种图像去噪方法，重点是基于块的去噪方法。然后，我们对基于块的去噪方法进行定量和定性的实验评估。从质量和计算时间方面分析基于块的图像去噪方法。
尽管基于块的图像去噪方法很复杂，但大多数基于块的图像去噪方法比其他方法表现更优。快速的块相似度测量产生了快速的基于块的图像去噪方法。
基于块的图像去噪方法可以有效地减少噪声并增强图像。基于块的图像去噪方法是最先进的图像去噪方法。
