论文标题
通过固定点投影（Red-Pro）进行降级来正规化
Regularization by Denoising via Fixed-Point Projection (RED-PRO)
论文作者
论文摘要
图像处理中的逆问题通常是作为优化任务施放的，包括数据保真度和稳定正则化项。最新的正规化策略利用了降级引擎的力量。这样的两种方法是插件先验（PNP）和通过denoing（红色）正规化。尽管两者都显示出最新的恢复任务，但其理论理由是不完整的。在本文中，我们的目标是在红色和PNP之间桥接，丰富对这两个框架的理解。为此，我们将红色作为凸优化问题进行了重新制定，该问题利用投影（Red-Pro）到定义点的删除授予denoisiser上。我们为此问题提供了一个简单的迭代解决方案，通过该解决方案，我们表明PNP近端梯度方法是红色PRO的一种特殊情况，同时为这两个框架与全球最佳解决方案的收敛提供了保证。此外，我们提出了红色pro的放松，允许处理有限的固定点集的DeNoisers。最后，我们为图像脱毛和超分辨率的任务演示了红色秘诀，相对于原始的红色框架显示了改进的结果。
Inverse problems in image processing are typically cast as optimization tasks, consisting of data-fidelity and stabilizing regularization terms. A recent regularization strategy of great interest utilizes the power of denoising engines. Two such methods are the Plug-and-Play Prior (PnP) and Regularization by Denoising (RED). While both have shown state-of-the-art results in various recovery tasks, their theoretical justification is incomplete. In this paper, we aim to bridge between RED and PnP, enriching the understanding of both frameworks. Towards that end, we reformulate RED as a convex optimization problem utilizing a projection (RED-PRO) onto the fixed-point set of demicontractive denoisers. We offer a simple iterative solution to this problem, by which we show that PnP proximal gradient method is a special case of RED-PRO, while providing guarantees for the convergence of both frameworks to globally optimal solutions. In addition, we present relaxations of RED-PRO that allow for handling denoisers with limited fixed-point sets. Finally, we demonstrate RED-PRO for the tasks of image deblurring and super-resolution, showing improved results with respect to the original RED framework.
