Of the image denoising models, the Perona–Malik one has captured the interest of most scholars for its ability to recover semantically important image features. However, this model emanates from an energy functional that is not entirely convex, a drawback that may cause undesirable solutions. In this work, we have attempted to convexify the functional by replacing its non-convex portion by the Charbonnier potential, which is strictly convex and has been reported by authors that it generates stable solutions. Extensive range of experiments have been conducted to demonstrate that our method produces compelling results.

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关于 Perona-Malik 扩散模型的凸化

在图像去噪模型中，Perona-Malik 模型因其能够恢复语义上重要的图像特征而引起了大多数学者的兴趣。然而，这个模型源自一个不完全凸的能量泛函，这是一个可能导致不受欢迎的解决方案的缺点。在这项工作中，我们试图通过用 Charbonnier 势替换其非凸部分来凸化泛函，Charbonnier 势是严格凸的，作者报告说它会生成稳定的解。已经进行了广泛的实验来证明我们的方法产生了令人信服的结果。