提出混合范数总变差模型，有效去除多种噪声及其组合。
An Improved Variational Method for Image Denoising
- 引入混合范数提升总变差模型对多类噪声的适应性。
- 理论证明解唯一且算法收敛，实验显示去噪效果优于传统TV模型。
- 适合需要保留边缘的图像去噪任务，如医学影像处理。
总变差（TV）方法是一种通过最小化图像像素强度变化来减少噪声的图像去噪技术，在图像处理和计算机视觉中广泛应用，因其能有效保持边缘并提升图像质量。本文提出一种混合范数总变差（MixTV）模型及其配套数值算法，该方法在去除多种噪声及其组合方面表现尤为出色。所提出的MixTV模型具有唯一解，相关数值算法保证收敛性。数值实验表明，与其它TV模型相比，MixTV在去噪效果和图像质量上均有显著提升。这些结果进一步增强了TV方法在图像处理中的实用性。项目页面详见 https://jing-en-huang.github.io/MixTV。
原文摘要 · Abstract (English)
The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of the image, which measures the variation in pixel intensities. The TV method has been widely applied in image processing and computer vision for its ability to preserve edges and enhance image quality. In this paper, we propose a Mixed-norm TV (MixTV) model for image denoising and the associated numerical algorithm to carry out the procedure, which is particularly effective in removing several types of noise and their combinations. Our MixTV admits a unique solution and the associated numerical algorithm guarantees convergence. Numerical experiments are demonstrated to show improved effectiveness and denoising quality compared to other TV models. Such encouraging results further enhance the utility of the TV method in image processing. Our project page is available at https://jing-en-huang.github.io/MixTV.
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