Slide 77
Slide 77 text
Blind deconvolution (2)
γf 0
c
θ2
c
zf
c
f
c
H
g
α0
c
α0
c
θ1
c
γh0
c
θ3
c
zh
c
h
©
d
d
g = h ∗ f + = Hf + = F h +
Simple priors:
p(f, h|g, θ) ∝ p(g|f, h, θ1
) p(f|θ2
) p(h|θ3
)
Unsupervised:
p(f, h, θ|g) ∝ p(g|f, h, θ1
) p(f|θ2
) p(h|θ3
) p(θ)
Sparsity enforcing prior for f:
p(f, zf , h, θ|g) ∝
p(g|f, h, θ1
) p(f|zf
) p(zf |θ2
) p(h|θ3
) p(θ)
Sparsity enforcing prior for h:
p(f, h, zh, θ|g) ∝
p(g|f, h, θ1
) p(f|θ2
) p(h|z) p(z|θ3
) p(θ)
Hierarchical models for both f and h:
p(f, zf , h, zh, θ|g) ∝
p(g|f, h, θ1
) p(f|zf
) p(zf |θ2
) p(h|zh
) p(zh|θ3
) p(θ)
JMAP:
(f, zf , h, zh, θ) = arg max
(f,zf ,h,zh,θ)
p(f, zf , h, zh, θ|g)
VBA: Approximate p(f, zf , h, zh, θ|g) by
q1
(f) q2
(zf
) q3
(h) q4
(zh
) q5
(θ)
A. Mohammad-Djafari, Inverse problems and Bayesian inference, Scube seminar, L2S, CentraleSupelec, March 27, 2015 77/77