%0 Journal Article %1 9018286 %A Song, G. %A Sun, Y. %A Liu, J. %A Wang, Z. %A Kamilov, U. S. %D 2020 %J IEEE Signal Processing Letters %K bayesian priors readings %P 1-1 %R 10.1109/LSP.2020.2977214 %T A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network %U https://ieeexplore.ieee.org/document/9018286 %X 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 image denoiser used as a prior. 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{9018286, 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 image denoiser used as a prior. 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.}, added-at = {2020-03-08T17:35:51.000+0100}, author = {{Song}, G. and {Sun}, Y. and {Liu}, J. and {Wang}, Z. and {Kamilov}, U. S.}, biburl = {https://www.bibsonomy.org/bibtex/2e405975629f8e8ebf011794c82241336/kirk86}, description = {A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network - IEEE Journals & Magazine}, doi = {10.1109/LSP.2020.2977214}, interhash = {8199d452dcfed7a7c0e7f9377bd5b080}, intrahash = {e405975629f8e8ebf011794c82241336}, issn = {1558-2361}, journal = {IEEE Signal Processing Letters}, keywords = {bayesian priors readings}, pages = {1-1}, timestamp = {2020-03-08T17:35:51.000+0100}, title = {A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network}, url = {https://ieeexplore.ieee.org/document/9018286}, year = 2020 }