Medical images have become omnipresent in diagnosis and therapy. However, they can be affected by various types of noise that reduce image quality and make the final diagnostic decision difficult. The main objective of this research is to effectively remove the noise while preserving the important image characteristics. This paper proposes a novel approach for image denoising based on discrete wavelet transform (DWT) with the selection of the best decomposition level and mother wavelet. Then, the thresholding function is carried out in the detail coefficients. Optimal thresholding is done using new optimization techniques such as the crow search algorithm and social spider optimization techniques. Finally, the inverse of DWT is applied to reconstruct the denoised image. The proposed method is evaluated using peak signal to noise ratio, mean square error, and the structural similarity index measure. The experimental results show the efficiency of the optimization-based denoising method over standard methods. Interesting results are obtained with all kinds of noise, and improvements about 30 dB can be reached with the Rician noise.

中文翻译：

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使用小波系数的最佳阈值化和选择最佳分解级别和母小波的医学图像去噪

医学图像在诊断和治疗中无处不在。然而，它们可能会受到各种类型的噪声的影响，这些噪声会降低图像质量并使最终的诊断决定变得困难。本研究的主要目标是在保留重要图像特征的同时有效去除噪声。本文提出了一种基于离散小波变换 (DWT) 的图像去噪新方法，可选择最佳分解级别和母小波。然后，在细节系数中执行阈值函数。最佳阈值是使用新的优化技术完成的，例如乌鸦搜索算法和社交蜘蛛优化技术。最后，应用 DWT 的逆来重建去噪图像。使用峰值信噪比评估所提出的方法，均方误差和结构相似性指数度量。实验结果表明，基于优化的去噪方法优于标准方法。使用各种噪声都获得了有趣的结果，使用 Rician 噪声可以达到约 30 dB 的改进。