New Fourth-Order Grayscale Indicator-Based Telegraph Diffusion Model for Image Despeckling
This work addresses image denoising for applications like SAR imaging, but it is incremental as it builds on existing PDE-based methods with a higher-order approach.
The authors tackled the problem of image despeckling, particularly for SAR images, by proposing a fourth-order nonlinear PDE model that integrates diffusion and wave properties to reduce noise while preserving details, resulting in better performance metrics like PSNR, MSSIM, and SI compared to existing second-order models.
Second-order PDE models have been widely used for suppressing multiplicative noise, but they often introduce blocky artifacts in the early stages of denoising. To resolve this, we propose a fourth-order nonlinear PDE model that integrates diffusion and wave properties. The diffusion process, guided by both the Laplacian and intensity values, reduces noise better than gradient-based methods, while the wave part keeps fine details and textures. The effectiveness of the proposed model is evaluated against two second-order anisotropic diffusion approaches using the Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) for images with available ground truth. For SAR images, where a noise-free reference is unavailable, the Speckle Index (SI) is used to measure noise reduction. Additionally, we extend the proposed model to study color images by applying the denoising process independently to each channel, preserving both structure and color consistency. The same quantitative metrics PSNR and MSSIM are used for performance evaluation, ensuring a fair comparison across grayscale and color images. In all the cases, our computed results produce better results compared to existing models in this genre.