/
proximalMaps.jl
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/
proximalMaps.jl
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#
# Manopt.jl – Proximal maps
#
# This file provides several proximal maps on manifolds or on small
# product manifolds, like M^2
#
# ---
# Manopt.jl - R. Bergmann – 2017-07-06
export proxDistance, proxTV, proxParallelTV, proxTV2, proxCollaborativeTV
@doc doc"""
y = proxDistance(M,λ,f,x [,p=2])
compute the proximal map $\operatorname{prox}_{\lambda\varphi}$ with
parameter λ of $\varphi(x) = \frac{1}{p}d_{\mathcal M}^p(f,x)$.
# Input
* `M` – a [`Manifold`](@ref) $\mathcal M$
* `λ` – the prox parameter
* `f` – an [`MPoint`](@ref) $f\in\mathcal M$ (the data)
* `x` – the argument of the proximal map
# Optional argument
* `p` – (`2`) exponent of the distance.
# Ouput
* `y` – the result of the proximal map of $\varphi$
"""
function proxDistance(M::mT,λ::Number,f::T,x::T,p::Int=2) where {mT <: Manifold, T <: MPoint}
d = distance(M,f,x)
if p==2
t = λ/(1+λ);
elseif p==1
if λ < d
t = λ/d;
else
t = 1.;
end
else
throw(ErrorException(
"Proximal Map of distance(M,f,x) not implemented for p=$(p) (requires p=1 or 2)"
))
end
return exp(M,x,log(M,x,f),t);
end
@doc doc"""
(y1,y2) = proxTV(M,λ,(x1,x2) [,p=1])
Compute the proximal map $\operatorname{prox}_{\lambda\varphi}$ of
$\varphi(x,y) = d_{\mathcal M}^p(x,y)$ with
parameter `λ`.
# Input
* `M` – a [`Manifold`](@ref)
* `λ` – a real value, parameter of the proximal map
* `(x1,x2)` – a tuple of two [`MPoint`](@ref)s,
# Optional
(default is given in brackets)
* `p` – (1) exponent of the distance of the TV term
# Ouput
* `(y1,y2)` – resulting tuple of [`MPoint`](@ref) of the
$\operatorname{prox}_{\lambda\varphi}($ `(x1,x2)` $)$
"""
function proxTV(M::mT,λ::Number, pointTuple::Tuple{P,P},p::Int=1)::Tuple{P,P} where {mT <: Manifold, P <: MPoint}
x1 = pointTuple[1];
x2 = pointTuple[2];
d = distance(M,x1,x2);
if p==1
t = min(0.5, λ/d);
elseif p==2
t = λ/(1+2*λ);
else
throw(ErrorException(
"Proximal Map of TV(M,x1,x2,p) not implemented for p=$(p) (requires p=1 or 2)"
))
end
return ( exp(M, x1, log(M, x1, x2), t), exp(M, x2, log(M, x2, x1), t) );
end
@doc doc"""
ξ = proxTV(M,λ,x [,p=1])
compute the proximal maps $\operatorname{prox}_{\lambda\varphi}$ of
all forward differences orrucirng in the power manifold array, i.e.
$\varphi(xi,xj) = d_{\mathcal M}^p(xi,xj)$ with `xi` and `xj` are array
elemets of `x` and `j = i+e_k`, where `e_k` is the $k$th unitvector.
The parameter `λ` is the prox parameter.
# Input
* `M` – a [`Manifold`](@ref)
* `λ` – a real value, parameter of the proximal map
* `x` – a [`PowPoint`](@ref).
# Optional
(default is given in brackets)
* `p` – (1) exponent of the distance of the TV term
# Ouput
* `y` – resulting of [`PowPoint`](@ref) with all mentioned proximal
points evaluated (in a cylic order).
"""
function proxTV(M::Power, λ::Number, x::PowPoint,p::Int=1)::PowPoint
R = CartesianIndices(M.powerSize)
d = length(M.powerSize)
maxInd = Tuple(last(R))
y = copy(x)
for k in 1:d # for all directions
ek = CartesianIndex(ntuple(i -> (i==k) ? 1 : 0, d) ) #k th unit vector
for l in 0:1
for i in R # iterate over all pixel
if (i[k] % 2) == l
I = [i.I...] # array of index
J = I .+ 1 .* (1:d .== k) #i + e_k is j
if all( J .<= maxInd ) # is this neighbor in range?
j = CartesianIndex(J...) # neigbbor index as Cartesian Index
(y[i],y[j]) = proxTV( M.manifold,λ,(y[i],y[j]),p) # Compute TV on these
end
end
end # i in R
end # even odd
end # directions
return y
end
@doc doc"""
ξ = proxParallelTV(M,λ,x [,p=1])
compute the proximal maps $\operatorname{prox}_{\lambda\varphi}$ of
all forward differences orrucirng in the power manifold array, i.e.
$\varphi(xi,xj) = d_{\mathcal M}^p(xi,xj)$ with `xi` and `xj` are array
elemets of `x` and `j = i+e_k`, where `e_k` is the $k$th unitvector.
The parameter `λ` is the prox parameter.
# Input
* `M` – a [`Power`](@ref) manifold
* `λ` – a real value, parameter of the proximal map
* `x` – a [`PowPoint`](@ref).
# Optional
(default is given in brackets)
* `p` – (`1`) exponent of the distance of the TV term
# Ouput
* `y` – resulting of Array [`PowPoint`](@ref)s with all mentioned proximal
points evaluated (in a parallel within the arrays elements).
*See also* [`proxTV`](@ref)
"""
function proxParallelTV(M::Power, λ::Number, x::Array{PowPoint{P,N},1}, p::Int=1)::Array{PowPoint{P,N},1} where {P <: MPoint, N}
R = CartesianIndices(getValue(x[1]))
d = ndims(getValue(x[1]))
if length(x) != 2*d
throw(ErrorException("The number of inputs from the array ($(length(x))) has to be twice the data dimensions ($(d))."))
end
maxInd = Tuple(last(R))
# create an array for even/odd splitted proxes along every dimension
y = reshape(deepcopy(x),d,2)
x = reshape(x,d,2)
for k in 1:d # for all directions
ek = CartesianIndex(ntuple(i -> (i==k) ? 1 : 0, d) ) #k th unit vector
for l in 0:1 # even odd
for i in R # iterate over all pixel
if (i[k] % 2) == l
I = [i.I...] # array of index
J = I .+ 1 .* (1:d .== k) #i + e_k is j
if all( J .<= maxInd ) # is this neighbor in range?
j = CartesianIndex(J...) # neigbbor index as Cartesian Index
# parallel means we apply each (direction even/odd) to a seperate copy of the data.
(y[k,l+1][i],y[k,l+1][j]) = proxTV( M.manifold,λ,(x[k,l+1][i],x[k,l+1][j]),p) # Compute TV on these
end
end
end # i in R
end # even odd
end # directions
return y[:] # return as onedimensional array
end
@doc doc"""
(y1,y2,y3) = proxTV2(M,λ,(x1,x2,x3),[p=1], kwargs...)
Compute the proximal map $\operatorname{prox}_{\lambda\varphi}$ of
$\varphi(x_1,x_2,x_3) = d_{\mathcal M}^p(c(x_1,x_3),x_2)$ with
parameter `λ`>0, where $c(x,z)$ denotes the mid point of a shortest
geodesic from `x1` to `x3` that is closest to `x2`.
# Input
* `M` – a manifold
* `λ` – a real value, parameter of the proximal map
* `(x1,x2,x3)` – a tuple of three [`MPoint`](@ref)s
* `p` – (`1`) exponent of the distance of the TV term
# Optional
`kwargs...` – parameters for the internal [`subGradientMethod`](@ref)
(if `M` is neither `Euclidean` nor `Circle`, since for these a closed form
is given)
# Output
* `(y1,y2,y3)` – resulting tuple of [`MPoint`](@ref)s of the proximal map
"""
function proxTV2(M::mT,λ,pointTuple::Tuple{P,P,P},p::Int=1;
stoppingCriterion::StoppingCriterion = stopAfterIteration(5),
kwargs...)::Tuple{P,P,P} where {mT <: Manifold, P <: MPoint}
if p != 1
throw(ErrorException(
"Proximal Map of TV2(M,λ,pT,p) not implemented for p=$(p) (requires p=1) on general manifolds."
))
end
PowX = PowPoint([pointTuple...])
PowM = Power(M,(3,))
xInit = PowX
F(x) = 1/2*distance(PowM,PowX,x)^2 + λ*costTV2(PowM,x)
∂F(x) = log(PowM,x,PowX) + λ*gradTV2(PowM,x)
xR = subGradientMethod(PowM,F,∂F,xInit;stoppingCriterion=stoppingCriterion, kwargs...)
return (getValue(xR)...,)
end
function proxTV2(M::Circle,λ,pointTuple::Tuple{S1Point,S1Point,S1Point},p::Int=1)::Tuple{S1Point,S1Point,S1Point}
w = [1., -2. ,1. ]
x = [getValue.(pointTuple)...]
if p==1 # Theorem 3.5 in Bergmann, Laus, Steidl, Weinmann, 2014.
m = min( λ, abs( symRem( sum( x .* w ) ) )/(dot(w,w)) )
s = sign( symRem(sum(x .* w)) )
return Tuple( S1Point.( symRem.( x .- m .* s .* w ) ) )
elseif p==2 # Theorem 3.6 ibd.
t = λ * symRem( sum( x .* w ) ) / (1 + λ*dot(w,w) )
return Tuple( S1Point.( symRem.( x - t.*w ) ) )
else
throw(ErrorException(
"Proximal Map of TV2(Circle,λ,pT,p) not implemented for p=$(p) (requires p=1 or 2)"
))
end
end
function proxTV2(M::Euclidean,λ,pointTuple::Tuple{RnPoint,RnPoint,RnPoint},p::Int=1)::Tuple{RnPoint,RnPoint,RnPoint}
w = [1., -2. ,1. ]
x = [getValue.(pointTuple)...]
if p==1 # Example 3.2 in Bergmann, Laus, Steidl, Weinmann, 2014.
m = min.(Ref(λ), abs.( x .* w ) / (dot(w,w)) )
s = sign.( sum(x .* w) )
return Tuple( RnPoint.( x .- m .* s .* w ) )
elseif p==2 # Theorem 3.6 ibd.
t = λ * sum( x .* w ) / (1 + λ*dot(w,w) )
return Tuple( RnPoint.( x - t.*w ) )
else
throw(ErrorException(
"Proximal Map of TV2(Euclidean,λ,pT,p) not implemented for p=$(p) (requires p=1 or 2)"
))
end
end
@doc doc"""
ξ = proxTV2(M,λ,x,[p])
compute the proximal maps $\operatorname{prox}_{\lambda\varphi}$ of
all centered second order differences orrucirng in the power manifold array, i.e.
$\varphi(x_k,x_i,x_j) = d_2(x_k,x_i.x_j)$, where $k,j$ are backward and forward
neighbors (along any dimension in the array of `x`).
The parameter `λ` is the prox parameter.
# Input
* `M` – a [`Manifold`](@ref)
* `λ` – a real value, parameter of the proximal map
* `x` – a [`PowPoint`](@ref).
# Optional
(default is given in brackets)
* `p` – (`1`) exponent of the distance of the TV term
# Ouput
* `y` – resulting of [`PowPoint`](@ref) with all mentioned proximal points
evaluated (in a cylic order).
"""
function proxTV2(M::Power, λ::Number, x::PowPoint,p::Int=1)::PowPoint
R = CartesianIndices(M.powerSize)
d = length(size(x))
minInd = [first(R).I...]
maxInd = [last(R).I...]
y = copy(x)
for k in 1:d # for all directions
for l in 0:1
for i in R # iterate over all pixel
if (i[k] % 3) == l
I = [i.I...] # array of index
JForward = I .+ 1 .* (1:d .== k) #i + e_k
JBackward = I .+ 1 .* (1:d .== k) # i - e_k
if all( JForward .<= maxInd ) && all( JBackward .>= minInd)
jForward = CartesianIndex{d}(JForward...) # neigbbor index as Cartesian Index
jBackward = CartesianIndex{d}(JForward...) # neigbbor index as Cartesian Index
(y[jBackward], y[i], y[jForward]) =
proxTV2( M.manifold, λ, (y[jBackward], y[i], y[jForward]),p) # Compute TV on these
end
end # if mod 3
end # i in R
end # for mod 3
end # directions
return y
end
@doc doc"""
proxCollaborativeTV(M,λ,x [,p=2,q=1])
compute the prox of the collaborative TV prox for x on the [`Power`](@ref)
manifold, i.e. of the function
```math
F^q(x) = \sum_{i\in\mathcal G}
\Bigl( \sum_{j\in\mathcal I_i}
\sum_{k=1^d} \lVert X_{i,j}\rVert_x^p\Bigr)^\frac{q/p},
```
where $\mathcal G$ is the set of indices for $x\in\mathcal M$ and $\mathcal I_i$
is the set of its forward neighbors.
This is adopted from the paper by Duran, Möller, Sbert, Cremers:
_Collaborative Total Variation: A General Framework for Vectorial TV Models_
(arxiv: [1508.01308](https://arxiv.org/abs/1508.01308)), where the most inner
norm is not on a manifold but on a vector space, see their Example 3 for
details.
"""
function proxCollaborativeTV(N::Power,λ::Float64,x::PowPoint,Ξ::PowTVector,p::Float64=2.,q::Float64=1.)
# Ξ = forwardLogs(M,x)
if length(size(x)) == 1
d = 1
s = 1
iRep = 1
else
d = size(x)[end]
s = length(size(x))-1
if s != d
throw( ErrorException( "the last dimension ($(d)) has to be equal to the number of the previous ones ($(s)) but its not." ))
end
iRep = [Integer.(ones(d))...,d]
end
if q==1 # Example 3 case 2
if p==1
normΞ = norm.(Ref(N.manifold), getValue(x), getValue(Ξ) )
return PowTVector( max.(normΞ .- λ, 0.) ./ ( (normΞ .== 0) .+ normΞ ) .* getValue(Ξ) )
elseif p==2 # Example 3 case 3
norms = sqrt.( sum( norm.(Ref(N.manifold),getValue(x),getValue(Ξ)).^2, dims=d+1) )
normΞ = repeat(norms,inner=iRep)
# if the norm is zero add 1 to avoid division by zero, also then the
# nominator is already (max(-λ,0) = 0) so it stays zero then
return PowTVector( max.(normΞ .- λ, 0.) ./ ( (normΞ .== 0) .+ normΞ ) .* getValue(Ξ) )
else
throw( ErrorException("The case p=$p, q=$q is not yet implemented"))
end
elseif q==Inf
if p==2
norms = sqrt.( sum( norm.(Ref(N.manifold),getValue(x),getValue(Ξ)).^2, dims=d+1) )
normΞ = repeat(norms,inner=iRep)
elseif p==1
norms = sum( norm.(Ref(N.manifold),getValue(x),getValue(Ξ)), dims=d+1)
normΞ = repeat(norms,inner=iRep)
elseif p==Inf
normΞ = norm.(Ref(N.manifold),getValue(x),getValue(Ξ))
else
throw( ErrorException("The case p=$p, q=$q is not yet implemented"))
end
return PowTVector(
λ .* getValue(Ξ) ./ max.(Ref(λ), normΞ)
)
end # end q
throw( ErrorException("The case p=$p, q=$q is not yet implemented"))
end
proxCollaborativeTV(N::Power,λ::Float64,x::PowPoint,Ξ::PowTVector,p::Int,q::Float64=1.) = proxCollaborativeTV(N,λ,x,Ξ,Float64(p),q)
proxCollaborativeTV(N::Power,λ::Float64,x::PowPoint,Ξ::PowTVector,p::Float64,q::Int) = proxCollaborativeTV(N,λ,x,Ξ,p,Float64(q))
proxCollaborativeTV(N::Power,λ::Float64,x::PowPoint,Ξ::PowTVector,p::Int,q::Int) = proxCollaborativeTV(N,λ,x,Ξ,Float64(p),Float64(q))