Noise removal in images denotes an interesting and a relatively challenging problem that has captured the attention of many scholars. Recent denoising methods focus on simultaneously restoring noisy images and recovering their semantic features (edges and contours). But preservation of textures, which facilitate interpretation and analysis of complex images, remains an open-ended research question. Classical methods (Total variation and Perona-Malik) and image denoising approaches based on deep neural networks tend to smudge fine details of images. Results from previous studies show that these methods, in addition, can introduce undesirable artifacts into textured images. To address the challenges, we have proposed an image denoising method based on anisotropic diffusion processes. The divergence term of our method contains a diffusion kernel that depends on the evolving image and its gradient magnitude to ensure effective preservation of edges, contours, and textures. Furthermore, a regularization term has been proposed to denoise images corrupted by multiplicative noise. Empirical results demonstrate that the proposed method generates images with higher perceptual and objective qualities.

图像中的噪声去除是一个有趣且相对具有挑战性的问题，已经引起了许多学者的关注。最近的去噪方法集中在同时恢复噪点图像并恢复其语义特征（边缘和轮廓）。但是，保留纹理以促进对复杂图像的解释和分析仍然是一个开放性的研究问题。基于深度神经网络的经典方法（总变异和Perona-Malik）和图像去噪方法往往会模糊图像的精细细节。先前研究的结果表明，这些方法还可以将不需要的伪像引入纹理图像。为了解决这些挑战，我们提出了一种基于各向异性扩散过程的图像去噪方法。我们方法的发散项包含一个扩散核，该扩散核取决于正在演化的图像及其梯度大小，以确保有效保留边缘，轮廓和纹理。此外，已经提出了正则化项以对被乘法噪声破坏的图像进行消噪。实验结果表明，所提出的方法能够生成具有较高感知和客观质量的图像。