  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.

Image and Video Processing