Talbi Mourad, Nasraoui Brahim, Alfaidi Arij
Center of Biotechnogy of Borj Cédria, Laboratory LMEEVED, Hammam-Lif, Tunisia.
Department of Computer Sciences, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia.
Curr Med Imaging. 2025 Jul 7. doi: 10.2174/0115734056365765250630140748.
The noise emergence in the digital image can occur throughout image acquisition, transmission, and processing steps. Consequently, eliminating the noise from the digital image is required before further processing. This study aims to denoise noisy images (including Magnetic Resonance Images ()) by employing our proposed image denoising approach.
This proposed approach is based on the Stationary Wavelet Transform () and the Dual-Tree Discrete Wavelet Transform (). The first step of this approach consists of applying the 2 - D Dual-Tree DWT to the noisy image to obtain noisy wavelet coefficients. The second step of this approach consists of denoising each of these coefficients by applying an SWT 2-D based denoising technique. The denoised image is finally obtained by applying the inverse of the 2-D Dual-Tree to the denoised coefficients obtained in the second step. The proposed image denoising approach is evaluated by comparing it to four denoising techniques existing in literature. The latters are the image denoising technique based on thresholding in the domain, the image denoising technique based on deep neural network, the image denoising technique based on soft thresholding in the domain of 2-D Dual-Tree DWT, and Non-local Means Filter.
The proposed denoising approach, and the other four techniques previously mentioned, are applied to a number of noisy grey scale images and noisy Magnetic Resonance Images (MRIs) and the obtained results are in terms of (Peak Signal to Noise Ratio), (Structural Similarity), (Normalized Mean Square Error) and Feature Similarity (). These results show that the proposed image denoising approach outperforms the other denoising techniques applied for our evaluation.
In comparison with the four denoising techniques applied for our evaluation, the proposed approach permits to obtain highest values of and and the lowest values of . Moreover, in cases where the noise level or , this approach permits the elimination of the noise from the noisy images and introduces slight distortions on the details of the original images. However, in case where or , this approach eliminates a great part of the noise and introduces some distortions on the original images.
The performance of this approach is proven by comparing it to four image denoising techniques existing in literature. These techniques are the denoising technique based on thresholding in the SWT-2D domain, the image denoising technique based on a deep neural network, the image denoising technique based on soft thresholding in the domain of Dual-Tree and the Non-local Means Filter. All these denoising techniques, including our approach, are applied to a number of noisy grey scale images and noisy , and the obtained results are in terms of (Peak Signal to Noise Ratio), (Structural Similarity), (Normalized Mean Square Error) and (Feature Similarity). These results show that this proposed approach outperforms the four denoising techniques applied for our evaluation.
数字图像中的噪声可能在图像采集、传输和处理的各个步骤中出现。因此，在进行进一步处理之前，需要去除数字图像中的噪声。本研究旨在通过采用我们提出的图像去噪方法对噪声图像（包括磁共振图像（MRIs））进行去噪。
该提出的方法基于平稳小波变换（SWT）和双树离散小波变换（Dual-Tree DWT）。此方法的第一步包括将二维双树离散小波变换应用于噪声图像以获得噪声小波系数。该方法的第二步包括通过应用基于二维平稳小波变换的去噪技术对每个系数进行去噪。最终通过将二维双树离散小波变换的逆变换应用于第二步中获得的去噪系数来得到去噪图像。通过将提出的图像去噪方法与文献中现有的四种去噪技术进行比较来评估该方法。这四种技术分别是基于SWT域阈值处理的图像去噪技术、基于深度神经网络的图像去噪技术、基于二维双树离散小波变换域软阈值处理的图像去噪技术以及非局部均值滤波器。
将提出的去噪方法以及上述其他四种技术应用于一些噪声灰度图像和噪声磁共振图像（MRIs），并根据峰值信噪比（PSNR）、结构相似性（SSIM）、归一化均方误差（NMSE）和特征相似性（FS）来获得结果。这些结果表明，提出的图像去噪方法优于我们用于评估的其他去噪技术。
与我们用于评估的四种去噪技术相比，提出的方法能够获得最高的PSNR和SSIM值以及最低的NMSE值。此外，在噪声水平较低（例如噪声水平为10或15）的情况下，该方法能够从噪声图像中去除噪声，并且对原始图像的细节引入轻微的失真。然而，在噪声水平较高（例如噪声水平为25或30）的情况下，该方法能够去除大部分噪声，但会对原始图像引入一些失真。
通过将该方法与文献中现有的四种图像去噪技术进行比较，证明了该方法的性能。这四种技术分别是基于二维平稳小波变换域阈值处理的去噪技术、基于深度神经网络的图像去噪技术、基于双树离散小波变换域软阈值处理的图像去噪技术以及非局部均值滤波器。所有这些去噪技术，包括我们提出的方法，都应用于一些噪声灰度图像和噪声磁共振图像，并根据峰值信噪比（PSNR）、结构相似性（SSIM）、归一化均方误差（NMSE）和特征相似性（FS）来获得结果。这些结果表明，提出的方法优于我们用于评估的四种去噪技术。
