  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.
