新的第四阶灰度指标基于电报扩散模型用于图像去斑
New Fourth-Order Grayscale Indicator-Based Telegraph Diffusion Model for Image Despeckling
AI总结：
本文提出一种第四阶非线性PDE模型，结合扩散和波特性，以改善去斑效果，通过PSNR、MSSIM和SI指标验证其有效性，并扩展至彩色图像处理。
摘要：
第二阶PDE模型广泛用于抑制乘性噪声，但常在去噪初期引入块状伪影。为解决此问题，我们提出一种第四阶非线性PDE模型，整合扩散和波特性。扩散过程由拉普拉斯和强度值引导，比梯度方法更有效减少噪声，而波部分保留细节数和纹理。所提模型的有效性通过PSNR和MSSIM评估，针对有地面真实数据的图像。对于SAR图像，由于无噪声参考，使用斑点指数(SI)衡量去噪效果。此外，我们扩展该模型以研究彩色图像，通过独立对每个通道应用去噪过程，保留结构和颜色一致性。相同定量指标PSNR和MSSIM用于性能评估，确保在灰度和彩色图像间公平比较。在所有情况下，计算结果均优于现有模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
