diff --git a/src/proximalMaps/ProxTV.jl b/src/proximalMaps/ProxTV.jl
index 8c4ebaef..fb78c274 100644
--- a/src/proximalMaps/ProxTV.jl
+++ b/src/proximalMaps/ProxTV.jl
@@ -13,11 +13,6 @@ A. Beck and T. Teboulle, "Fast Gradient-Based Algorithms for Constrained
 Total Variation Image Denoising
 and Deblurring Problems", IEEE Trans. Image Process. 18(11), 2009
 
-Reference for the FGP algorithm:
-A. Beck and T. Teboulle, "Fast Gradient-Based Algorithms for Constrained
-Total Variation Image Denoising
-and Deblurring Problems", IEEE Trans. Image Process. 18(11), 2009
-
 # Arguments
 * `λ::T`                    - regularization parameter
 
@@ -26,16 +21,16 @@ and Deblurring Problems", IEEE Trans. Image Process. 18(11), 2009
 * `dims`                    - Dimension to perform the TV along. If `Integer`, the Condat algorithm is called, and the FDG algorithm otherwise.
 * `iterationsTV=20`         - number of FGP iterations
 """
-struct TVRegularization{T, N, TI} <: AbstractParameterizedRegularization{T} where {N, TI<:Integer}
+struct TVRegularization{T,N,TI} <: AbstractParameterizedRegularization{T} where {N,TI<:Integer}
   λ::T
   dims
   shape::NTuple{N,TI}
   iterationsTV::Int64
 end
-TVRegularization(λ; shape = (0,), dims = 1:length(shape), iterationsTV = 10, kargs...) = TVRegularization(λ, dims, shape, iterationsTV)
+TVRegularization(λ; shape=(0,), dims=1:length(shape), iterationsTV=10, kargs...) = TVRegularization(λ, dims, shape, iterationsTV)
 
 
-mutable struct TVParams{Tc, matT}
+mutable struct TVParams{Tc,matT}
   pq::Vector{Tc}
   rs::Vector{Tc}
   pqOld::Vector{Tc}
@@ -44,7 +39,7 @@ mutable struct TVParams{Tc, matT}
 end
 
 function TVParams(shape, T::Type=Float64; dims=1:length(shape))
-  return TVParams(Vector{T}(undef,prod(shape)); shape=shape, dims=dims)
+  return TVParams(Vector{T}(undef, prod(shape)); shape=shape, dims=dims)
 end
 
 function TVParams(x::AbstractVector{Tc}; shape, dims=1:length(shape)) where {Tc}
@@ -52,7 +47,7 @@ function TVParams(x::AbstractVector{Tc}; shape, dims=1:length(shape)) where {Tc}
 
   # allocate storage
   xTmp = similar(x)
-  pq = similar(x, size(∇,1))
+  pq = similar(x, size(∇, 1))
   rs = similar(pq)
   pqOld = similar(pq)
 
@@ -66,64 +61,59 @@ end
 
 Proximal map for TV regularization. Calculated with the Condat algorithm if the TV is calculated only along one dimension and with the Fast Gradient Projection algorithm otherwise.
 """
-prox!(reg::TVRegularization, x::Vector{Tc}, λ::T) where {T, Tc <: Union{T, Complex{T}}} = proxTV!(x, λ, shape = reg.shape, dims = reg.dims)
+prox!(reg::TVRegularization, x::Vector{Tc}, λ::T) where {T,Tc<:Union{T,Complex{T}}} = proxTV!(x, λ, shape=reg.shape, dims=reg.dims, iterationsTV=reg.iterationsTV)
 
 function proxTV!(x, λ; shape, dims=1:length(shape), kwargs...) # use kwargs for shape and dims
   return proxTV!(x, λ, shape, dims; kwargs...) # define shape and dims w/o kwargs to enable multiple dispatch on dims
 end
 
-function proxTV!(x::AbstractVector{T}, λ::T, shape, dims::Integer; kwargs...) where {T <: Real}
+function proxTV!(x::AbstractVector{T}, λ::T, shape, dims::Integer; kwargs...) where {T<:Real}
   x_ = reshape(x, shape)
+  i = CartesianIndices((ones(Int, dims - 1)..., 0:shape[dims]-1, ones(Int, length(shape) - dims)...))
 
-  if dims == 1
-    for j ∈ CartesianIndices(shape[dims+1:end])
-      @views @inbounds tv_denoise_1d_condat!(x_[:,j], shape[dims], λ)
-    end
-  elseif dims == length(shape)
-    for i ∈ CartesianIndices(shape[1:dims-1])
-      @views @inbounds tv_denoise_1d_condat!(x_[i,:], shape[dims], λ)
-    end
-  else
-    for j ∈ CartesianIndices(shape[dims+1:end]), i ∈ CartesianIndices(shape[1:dims-1])
-      @views @inbounds tv_denoise_1d_condat!(x_[i,:,j], shape[dims], λ)
-    end
+  Threads.@threads for j ∈ CartesianIndices((shape[1:dims-1]..., 1, shape[dims+1:end]...))
+    @views @inbounds tv_denoise_1d_condat!(x_[j.+i], shape[dims], λ)
   end
   return x
 end
 
-# reinterpret complex-valued vector as 2xN matrix and change the shape etc accordingly
-function proxTV!(x::AbstractVector{Tc}, λ::T, shape, dims::Integer; kwargs...) where {T <: Real, Tc <: Complex{T}}
-  proxTV!(vec(reinterpret(reshape, T, x)), λ, shape=(2, shape...), dims=(dims+1) )
-  return x
-end
-
-function proxTV!(x::AbstractVector{Tc}, λ::T, shape, dims; iterationsTV=20, tvpar=TVParams(x; shape=shape, dims=dims), kwargs...) where {T <: Real,Tc <: Union{T, Complex{T}}}
-  return proxTV!(x,λ,tvpar; iterationsTV=iterationsTV)
+function proxTV!(x::AbstractVector{Tc}, λ::T, shape, dims; iterationsTV=10, tvpar=TVParams(x; shape=shape, dims=dims), kwargs...) where {T<:Real,Tc<:Union{T,Complex{T}}}
+  return proxTV!(x, λ, tvpar; iterationsTV=iterationsTV)
 end
 
-function proxTV!(x::AbstractVector{Tc}, λ::T, p::TVParams{Tc}; iterationsTV=20, kwargs...) where {T <: Real, Tc <: Union{T, Complex{T}}}
+function proxTV!(x::AbstractVector{Tc}, λ::T, p::TVParams{Tc}; iterationsTV=10, kwargs...) where {T<:Real,Tc<:Union{T,Complex{T}}}
+  @assert length(p.xTmp) == length(x)
   # initialize dual variables
-  p.xTmp  .= 0
-  p.pq    .= 0
-  p.rs    .= 0
+  p.xTmp .= 0
+  p.pq .= 0
+  p.rs .= 0
   p.pqOld .= 0
 
   t = one(T)
   for _ = 1:iterationsTV
-    p.pqOld .= p.pq
+    pqTmp = p.pqOld
+    p.pqOld = p.pq
+    p.pq = p.rs
 
     # gradient projection step for dual variables
-    p.xTmp .= x
+    Threads.@threads for i ∈ eachindex(p.xTmp, x)
+      @inbounds p.xTmp[i] = x[i]
+    end
     mul!(p.xTmp, transpose(p.∇), p.rs, -λ, 1) # xtmp = x-λ*transpose(∇)*rs
-    p.pq .= p.rs
-    mul!(p.pq, p.∇, p.xTmp, 1/(8λ), 1) # rs = ∇*xTmp/(8λ)
+    mul!(p.pq, p.∇, p.xTmp, 1 / (8λ), 1) # rs = ∇*xTmp/(8λ)
+
     restrictMagnitude!(p.pq)
 
     # form linear combination of old and new estimates
     tOld = t
-    t = (1 + sqrt(1+4*tOld^2)) / 2
-    t2 = ((tOld-1)/t)
-    p.rs .= (1+t2) .* p.pq .- t2 .* p.pqOld
+    t = (1 + sqrt(1 + 4 * tOld^2)) / 2
+    t2 = ((tOld - 1) / t)
+    t3 = 1 + t2
+
+    p.rs = pqTmp
+    Threads.@threads for i ∈ eachindex(p.rs, p.pq, p.pqOld)
+      @inbounds p.rs[i] = t3 * p.pq[i] - t2 * p.pqOld[i]
+    end
   end
 
   mul!(x, transpose(p.∇), p.pq, -λ, one(Tc)) # x .-= λ*transpose(∇)*pq
@@ -132,7 +122,9 @@ end
 
 # restrict x to a number smaller then one
 function restrictMagnitude!(x)
-  x ./= max.(1, abs.(x))
+  Threads.@threads for i in eachindex(x)
+    @inbounds x[i] /= max(1, abs(x[i]))
+  end
 end
 
 """
@@ -140,7 +132,7 @@ end
 
 returns the value of the TV-regularization term.
 """
-function norm(reg::TVRegularization, x::Vector{Tc},λ::T) where {T <: Real, Tc <: Union{T, Complex{T}}}
+function norm(reg::TVRegularization, x::Vector{Tc}, λ::T) where {T<:Real,Tc<:Union{T,Complex{T}}}
   ∇ = GradientOp(Tc; shape=reg.shape, dims=reg.dims)
-  return λ * norm(∇*x, 1)
+  return λ * norm(∇ * x, 1)
 end