Amiri Golilarz Noorbakhsh, Gao Hui, Kumar Rajesh, Ali Liaqat, Fu Yan, Li Chun
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Front Neurosci. 2020 Jul 22;14:728. doi: 10.3389/fnins.2020.00728. eCollection 2020.
This paper presents a unique approach for wavelet-based MRI brain image de-noising. Adaptive soft and hard threshold functions are first proposed to improve the results of standard soft and hard threshold functions for image de-noising in the wavelet domain. Then, we applied the newly emerged improved adaptive generalized Gaussian distributed oriented threshold function (improved AGGD) on the MRI images to improve the results of the adaptive soft and hard threshold functions and also to display, this non-linear and data-driven function can work promisingly even in de-noising the medical images. The most important characteristic of this function is that it is dependent on the image since it is combined with an adaptive generalized Gaussian distribution function.Traditional thresholding neural network (TNN) and optimized based noise reduction have good results but fail to keep the visual quality and may blur some parts of an image. In TNN and optimized based image de-noising, it was required to use Least-mean-square (LMS) learning and optimization algorithms, respectively to find the optimum threshold value and parameters of the threshold functions which was time consuming. To address these issues, the improved AGGD based image de-noising approach is introduced to enhance the qualitative and quantitative performance of the above mentioned image de-noising techniques. De-noising using improved AGGD threshold function provides better results in terms of Peak Signal to Noise Ratio (PSNR) and also faster processing time since there is no need to use any Least-mean-square (LMS) learning and optimization algorithms for obtaining the optimum value and parameters of the thresholding functions. The experimental results indicate that image de-noising using improved AGGD threshold performs pretty well comparing with the adaptive threshold, standard threshold, improved wavelet threshold, and the optimized based noise reduction methods.
本文提出了一种基于小波的MRI脑图像去噪的独特方法。首先提出了自适应软阈值函数和硬阈值函数，以改善小波域中用于图像去噪的标准软阈值函数和硬阈值函数的结果。然后，我们将新出现的改进的自适应广义高斯分布定向阈值函数（改进的AGGD）应用于MRI图像，以改善自适应软阈值函数和硬阈值函数的结果，并且还表明，这种非线性和数据驱动的函数即使在对医学图像进行去噪时也能有很好的效果。该函数最重要的特征是它依赖于图像，因为它与自适应广义高斯分布函数相结合。传统的阈值神经网络（TNN）和基于优化的降噪方法有很好的效果，但无法保持视觉质量，并且可能会模糊图像的某些部分。在TNN和基于优化的图像去噪中，分别需要使用最小均方（LMS）学习和优化算法来找到阈值函数的最佳阈值和参数，这很耗时。为了解决这些问题，引入了基于改进的AGGD的图像去噪方法，以提高上述图像去噪技术的定性和定量性能。使用改进的AGGD阈值函数进行去噪在峰值信噪比（PSNR）方面提供了更好的结果，并且处理时间更快，因为无需使用任何最小均方（LMS）学习和优化算法来获得阈值函数的最佳值和参数。实验结果表明，与自适应阈值、标准阈值、改进的小波阈值和基于优化的降噪方法相比，使用改进的AGGD阈值进行图像去噪表现良好。
