Recent work has shown the effectiveness of the plug-and-play priors (PnP) framework for regularized image reconstruction. However, the performance of PnP depends on the quality of the denoisers used as priors. In this letter, we design a novel PnP denoising prior, called multiple self-similarity net (MSSN), based on the recurrent neural network (RNN) with self-similarity matching using multi-head attention mechanism. Unlike traditional neural net denoisers, MSSN exploits different types of relationships among non-local and repeating features to remove the noise in the input image. We numerically evaluate the performance of MSSN as a module within PnP for solving magnetic resonance (MR) image reconstruction. Experimental results show the stable convergence and excellent performance of MSSN for reconstructing images from highly compressive Fourier measurements.

中文翻译：

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一种新的基于多重自相似网络的循环即插即用先验

最近的工作表明了即插即用先验 (PnP) 框架对于正则化图像重建的有效性。但是，PnP 的性能取决于用作先验的降噪器的质量。在这封信中，我们设计了一种新颖的 PnP 去噪先验，称为多重自相似网络 (MSSN)，它基于使用多头注意机制具有自相似匹配的循环神经网络 (RNN)。与传统的神经网络降噪器不同，MSSN 利用非局部和重复特征之间的不同类型关系来去除输入图像中的噪声。我们数值评估了 MSSN 作为 PnP 中解决磁共振 (MR) 图像重建模块的性能。