提出可收敛的插件式去噪算法，让去噪器直接替代优化步骤。
From the Gradient-Step Denoiser to the Proximal Denoiser and their associated convergent Plug-and-Play algorithms
- 用训练好的梯度步去噪器替代优化中的显式算子。
- 去噪器在保持顶尖去噪效果的同时实现数学收敛性。
- 适合需要稳定迭代的图像重建与逆问题求解任务。
本文分析了梯度步去噪器及其在插件式算法中的应用。插件式优化框架使用现成的去噪器替代图像先验中的邻近算子或梯度下降算子，该先验通常隐式存在且无法显式表达。梯度步去噪器通过训练，精确模拟某一显式函数的梯度下降或邻近算子，同时保持最先进的去噪性能。
原文摘要 · Abstract (English)
In this paper we analyze the Gradient-Step Denoiser and its usage in Plug-and-Play algorithms. The Plug-and-Play paradigm of optimization algorithms uses off the shelf denoisers to replace a proximity operator or a gradient descent operator of an image prior. Usually this image prior is implicit and cannot be expressed, but the Gradient-Step Denoiser is trained to be exactly the gradient descent operator or the proximity operator of an explicit functional while preserving state-of-the-art denoising capabilities.
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