Diffusion-Driven Image Denoising Model with Texture Preservation Capabilities

Abstract

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.