In this paper, a novel denoising approach based on optimal trilateral filtering using Grey Wolf Optimization (GWO) is proposed. At first, a database of noisy images are generated by adding Gaussian noise, Salt & Pepper noise and Random noise to the captured image. The filtering of noisy images are performed by Block-matching and 3D filtering (BM3D) algorithm over the components of image obtained through the moving frame approach. Then, using optimal trilateral filtering, the denoised images are reconstructed. Therefore, by using a two-level filtering approach such as Moving frame-based Block-matching and 3D filtering (BM3D) and Optimal trilateral filtering the noisy images are decomposed. The proposed optimal trilateral filter employs Grey Wolf Optimization algorithm for selecting the parameters optimally to improve the efficiency of filtering method which also reduces the time required for manual computation. The performance of the proposed image denoising algorithm is analyzed using multiple datasets and the analysis of results were done in contrast with existing conventional approaches. The results validated that the optimal trilateral filtering approach outperforms other conventional methods in terms of Mean-Square Error (MSE) and the Peak Signal-to-Noise Ratio (PSNR).

在本文中，提出了一种新的基于使用灰狼优化（GWO）的最佳三边滤波的去噪方法。首先，通过将高斯噪声，盐和胡椒噪声以及随机噪声添加到捕获的图像来生成噪声图像数据库。噪声图像的滤波是通过块匹配和3D滤波（BM3D）算法对通过移动帧方法获得的图像分量执行的。然后，使用最佳三边滤波，重建去噪图像。因此，通过使用诸如基于运动帧的块匹配和3D滤波（BM3D）和最佳三边滤波之类的两级滤波方法，可以对噪点图像进行分解。提出的最优三边滤波器采用格雷夫优化算法对参数进行最优选择，以提高滤波效率，同时也减少了人工计算的时间。使用多个数据集分析了提出的图像去噪算法的性能，并与现有的常规方法进行了结果分析。结果证实，在均方误差（MSE）和峰值信噪比（PSNR）方面，最佳三边滤波方法优于其他常规方法。