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2016

Denoising based Vector Approximate Message Passing

Schniter, Philip, Rangan, Sundeep, Fletcher, Alyson

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The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery.

But AMP diverges with small deviations from the i.i.d.-Gaussian assumption on the measurement matrix.
Recently, the vector AMP (VAMP) algorithm has been proposed to fix this problem.
In this work, we show that the benefits of VAMP extend to D-VAMP.

Denoising based Vector Approximate Message Passing · Around

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K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. , vol. 16, no. 8, pp. 2080–2095, 2007
2007
Earlier work this paper cites.
D. L. Donoho, A. Maleki, and A. Montanari, “Message passing algorithms for compressed sensing,” Proc. Nat. Acad. Sci. , vol. 106, no. 45, pp. 18 914–18 919, Nov. 2009
2009
Earlier work this paper cites.

Similar

C. A. Metzler, A. Maleki, and R. G. Baraniuk, “From denoising to compressed sensing,” IEEE Trans. Inform. Theory , vol. 62, no. 9, pp. 5117–5144, 2016
2016
Cited alongside, same era.
S. Rangan, P. Schniter, and A. K. Fletcher, “Vector approximate message passing,” arXiv:1610.03082 , 2016
Original
2016
Cited alongside, same era.

Then

A. K. Fletcher and P. Schniter, “Learning and free energies for vector approximate message passing,” arXiv:1602.08207 , 2016
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2016
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