TR2020-045
Learning Plug-and-Play Proximal Quasi-Newton Denoisers
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- "Learning Plug-and-Play Proximal Quasi-Newton Denoisers", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9054537, April 2020, pp. 8896-8900.BibTeX TR2020-045 PDF Video
- @inproceedings{Al-Shabili2020apr,
- author = {Al-Shabili, Abdullah and Mansour, Hassan and Boufounos, Petros T.},
- title = {Learning Plug-and-Play Proximal Quasi-Newton Denoisers},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2020,
- pages = {8896--8900},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP40776.2020.9054537},
- issn = {2379-190X},
- isbn = {978-1-5090-6631-5},
- url = {https://www.merl.com/publications/TR2020-045}
- }
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- "Learning Plug-and-Play Proximal Quasi-Newton Denoisers", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9054537, April 2020, pp. 8896-8900.
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MERL Contacts:
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Research Areas:
Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing
Abstract:
Plug-and-play (PnP) denoising for solving inverse problems has received significant attention recently thanks to its state of the art signal reconstruction performance. However, the performance improvement hinges on carefully choosing the noise level of the Gaussian denoiser and the descent step size in every iteration. We propose a strategy for training a Gaussian denoiser inspired by an unfolded proximal quasi-Newton algorithm, where the noise level of the input signal to the denoiser is estimated in each iteration and at every entry in the signal. Our scheme deploys a small convolutional neural network (mini-CNN) to estimate an element-wise noise level, mimicking a diagonal approximation of the Hessian matrix in quasi-Newton methods. Empirical simulation results on image deblurring demonstrate that our proposed approach achieves approximately 1dB improvement over state of the art methods, such as, BM3D-PnP and proximal gradient descent-PnP that are supplied with the true noise level, as well as over an end-to-end retrained FFDNet architecture that was trained to estimate the noise level and recover the deblurred images.
Related News & Events
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NEWS MERL presenting 13 papers and an industry talk at ICASSP 2020 Date: May 4, 2020 - May 8, 2020
Where: Virtual Barcelona
MERL Contacts: Petros T. Boufounos; Chiori Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Yanting Ma; Hassan Mansour; Philip V. Orlik; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing, Speech & AudioBrief- MERL researchers are presenting 13 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held virtually from May 4-8, 2020. Petros Boufounos is also presenting a talk on the Computational Sensing Revolution in Array Processing (video) in ICASSP’s Industry Track, and Siheng Chen is co-organizing and chairing a special session on a Signal-Processing View of Graph Neural Networks.
Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, array processing, and parameter estimation. Videos for all talks are available on MERL's YouTube channel, with corresponding links in the references below.
This year again, MERL is a sponsor of the conference and will be participating in the Student Job Fair; please join us to learn about our internship program and career opportunities.
ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year. Originally planned to be held in Barcelona, Spain, ICASSP has moved to a fully virtual setting due to the COVID-19 crisis, with free registration for participants not covering a paper.
- MERL researchers are presenting 13 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held virtually from May 4-8, 2020. Petros Boufounos is also presenting a talk on the Computational Sensing Revolution in Array Processing (video) in ICASSP’s Industry Track, and Siheng Chen is co-organizing and chairing a special session on a Signal-Processing View of Graph Neural Networks.