提出两种新迭代方法，有效去除盐椒噪声。
Computationally iterative methods for salt-and-pepper denoising
- 线性化非线性方程，结合两阶段迭代框架
- 实验验证方法正确且高效
- 适合图像去噪研究者参考
图像恢复指重建被噪声、破坏或缺失部分的图像，是一个病态逆问题。通常需假设特定正则项和图像退化模型以使问题适定。基于此假设，图像恢复问题可建模为带或不带正则化的线性或非线性优化问题，可通过迭代方法求解。本文通过线性化非线性方程组，并将其耦合至两阶段迭代框架中，提出两种不同的迭代方法。定性和定量实验结果证明了所提方法的正确性与高效性。
原文摘要 · Abstract (English)
Image restoration refers to the process of reconstructing noisy, destroyed, or missing parts of an image, which is an ill-posed inverse problem. A specific regularization term and image degradation are typically assumed to achieve well-posedness. Based on the underlying assumption, an image restoration problem can be modeled as a linear or non-linear optimization problem with or without regularization, which can be solved by iterative methods. In this work, we propose two different iterative methods by linearizing a system of non-linear equations and coupling them with a two-phase iterative framework. The qualitative and quantitative experimental results demonstrate the correctness and efficiency of the proposed methods.
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