# A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network

## TL;DR

This paper introduces MSSN, a novel RNN-based denoising prior utilizing self-similarity and attention mechanisms, enhancing plug-and-play image reconstruction, especially in MRI from compressed measurements.

## Contribution

The paper proposes MSSN, a new RNN-based denoiser leveraging self-similarity and attention, improving PnP image reconstruction performance.

## Key findings

- MSSN achieves stable convergence in MRI reconstruction.
- MSSN outperforms traditional denoisers in highly compressive scenarios.
- Experimental results demonstrate high-quality image recovery from limited measurements.

## Abstract

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.