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.
@article{arxiv.1907.11793,
title = {A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network},
author = {Guangxiao Song and Yu Sun and Jiaming Liu and Zhijie Wang and Ulugbek S. Kamilov},
journal= {arXiv preprint arXiv:1907.11793},
year = {2020}
}