diff --git a/src/Regularization/PlugAndPlayRegularization.jl b/src/Regularization/PlugAndPlayRegularization.jl
index ae2858fd..2772afb2 100644
--- a/src/Regularization/PlugAndPlayRegularization.jl
+++ b/src/Regularization/PlugAndPlayRegularization.jl
@@ -24,7 +24,7 @@ struct PlugAndPlayRegularization{T, M, I} <: AbstractParameterizedRegularization
 end
 PlugAndPlayRegularization(model, shape; kwargs...) = PlugAndPlayRegularization(one(Float32); kwargs..., model = model, shape = shape)
 
-function prox!(self::PlugAndPlayRegularization, x::AbstractArray{Tc}, λ::T) where {T, Tc <: Complex{T}}
+function prox!(self::PlugAndPlayRegularization, x::AbstractArray{Complex{T}}, λ::T) where {T <: Real}
     if self.ignoreIm
         x[:] = prox!(self, real.(x), λ) + imag.(x) * one(T)im
     else
@@ -33,7 +33,7 @@ function prox!(self::PlugAndPlayRegularization, x::AbstractArray{Tc}, λ::T) whe
     return x
 end
 
-function prox!(self::PlugAndPlayRegularization, x::AbstractArray{T}, λ::T) where {T}
+function prox!(self::PlugAndPlayRegularization, x::AbstractArray{T}, λ::T) where {T <: Real}
 
     if λ != self.λ && (λ < 0.0 || λ > 1.0)
         temp = clamp(λ, zero(T), one(T))
diff --git a/src/Regularization/Regularization.jl b/src/Regularization/Regularization.jl
index 760114b5..768c37a5 100644
--- a/src/Regularization/Regularization.jl
+++ b/src/Regularization/Regularization.jl
@@ -27,9 +27,9 @@ norm(reg::AbstractParameterizedRegularization, x::AbstractArray) = norm(reg, x,
 return the regularization parameter `λ` of `reg`
 """
 λ(reg::AbstractParameterizedRegularization) = reg.λ
-# Conversion
-prox!(reg::AbstractParameterizedRegularization, x::AbstractArray{Tc}, λ) where {T, Tc<:Union{T, Complex{T}}} = prox!(reg, x, convert(T, λ))
-norm(reg::AbstractParameterizedRegularization, x::AbstractArray{Tc}, λ) where {T, Tc<:Union{T, Complex{T}}} = norm(reg, x, convert(T, λ))
+# Conversion (https://github.com/JuliaLang/julia/issues/52978#issuecomment-1900698430)
+prox!(reg::R, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::otherT) where {R<:AbstractParameterizedRegularization, T <: Real, otherT} = prox!(reg, x, convert(T, λ))
+norm(reg::R, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::otherT) where {R<:AbstractParameterizedRegularization, T <: Real, otherT} = norm(reg, x, convert(T, λ))
 
 """
     prox!(regType::Type{<:AbstractParameterizedRegularization}, x, λ; kwargs...)
diff --git a/src/proximalMaps/ProxL1.jl b/src/proximalMaps/ProxL1.jl
index 53c59634..61ca38f3 100644
--- a/src/proximalMaps/ProxL1.jl
+++ b/src/proximalMaps/ProxL1.jl
@@ -15,8 +15,8 @@ end
 
 performs soft-thresholding - i.e. proximal map for the Lasso problem.
 """
-function prox!(::L1Regularization, x::AbstractArray{Tc}, λ::T) where {T, Tc <: Union{T, Complex{T}}}
-  ε = eps(typeof(λ))
+function prox!(::L1Regularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real}
+  ε = eps(T)
   x .= max.((abs.(x).-λ),0) .* (x.+ε)./(abs.(x).+ε)
   return x
 end
@@ -26,7 +26,7 @@ end
 
 returns the value of the L1-regularization term.
 """
-function norm(::L1Regularization, x::AbstractArray{Tc}, λ::T) where {T, Tc <: Union{T, Complex{T}}}
+function norm(::L1Regularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real}
   l1Norm = λ*norm(x,1)
   return l1Norm
 end
diff --git a/src/proximalMaps/ProxL2.jl b/src/proximalMaps/ProxL2.jl
index fd1abe08..bc8ecc7c 100644
--- a/src/proximalMaps/ProxL2.jl
+++ b/src/proximalMaps/ProxL2.jl
@@ -15,7 +15,7 @@ end
 
 performs the proximal map for Tikhonov regularization.
 """
-function prox!(::L2Regularization, x::AbstractArray{Tc}, λ::T) where {T, Tc <: Union{T, Complex{T}}}
+function prox!(::L2Regularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real}
   x[:] .*= 1. ./ (1. .+ 2. .*λ)#*x
   return x
 end
@@ -25,8 +25,8 @@ end
 
 returns the value of the L2-regularization term
 """
-norm(::L2Regularization, x::AbstractArray{Tc}, λ::T) where {T, Tc <: Union{T, Complex{T}}} = λ*norm(x,2)^2
-function norm(::L2Regularization, x::AbstractArray{Tc}, λ::AbstractArray{T}) where {T, Tc <: Union{T, Complex{T}}}
+norm(::L2Regularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real} = λ*norm(x,2)^2
+function norm(::L2Regularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::AbstractArray{T}) where {T <: Real}
   res = zero(real(eltype(x)))
   for i in eachindex(x)
     res+= λ[i]*abs2(x[i])
diff --git a/src/proximalMaps/ProxL21.jl b/src/proximalMaps/ProxL21.jl
index 40d2a77f..4452a9f4 100644
--- a/src/proximalMaps/ProxL21.jl
+++ b/src/proximalMaps/ProxL21.jl
@@ -23,11 +23,11 @@ L21Regularization(λ; slices::Int64 = 1, kargs...) = L21Regularization(λ, slice
 
 performs group-soft-thresholding for l1/l2-regularization.
 """
-function prox!(reg::L21Regularization, x::AbstractArray{Tc},λ::T) where {T, Tc <: Union{T, Complex{T}}}
+function prox!(reg::L21Regularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}},λ::T) where {T <: Real}
   return proxL21!(x, λ, reg.slices)
 end
 
-function proxL21!(x::AbstractArray{T}, λ::Float64, slices::Int64) where T
+function proxL21!(x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T, slices::Int64) where T
   sliceLength = div(length(x),slices)
   groupNorm = [norm(x[i:sliceLength:end]) for i=1:sliceLength]
   x[:] = [ x[i]*max( (groupNorm[mod1(i,sliceLength)]-λ)/groupNorm[mod1(i,sliceLength)],0 ) for i=1:length(x)]
@@ -39,7 +39,7 @@ end
 
 return the value of the L21-regularization term.
 """
-function norm(reg::L21Regularization, x::AbstractArray{Tc}, λ::T) where {T, Tc <: Union{T, Complex{T}}}
+function norm(reg::L21Regularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real}
   sliceLength = div(length(x),reg.slices)
   groupNorm = [norm(x[i:sliceLength:end]) for i=1:sliceLength]
   return λ*norm(groupNorm,1)
diff --git a/src/proximalMaps/ProxLLR.jl b/src/proximalMaps/ProxLLR.jl
index f65815ef..232a7ac6 100644
--- a/src/proximalMaps/ProxLLR.jl
+++ b/src/proximalMaps/ProxLLR.jl
@@ -28,7 +28,8 @@ LLRRegularization(λ;  shape::NTuple{N,TI}, blockSize::NTuple{N,TI} = ntuple(_ -
 
 performs the proximal map for LLR regularization using singular-value-thresholding
 """
-function prox!(reg::LLRRegularization{TR, N, TI}, x::AbstractArray{Tc}, λ::T) where {TR, N, TI, T, Tc <: Union{T, Complex{T}}}
+function prox!(reg::LLRRegularization{TR, N, TI}, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {TR, N, TI, T <: Real}
+    Tc = eltype(x)
     shape = reg.shape
     blockSize = reg.blockSize
     randshift = reg.randshift
@@ -92,7 +93,7 @@ end
 
 returns the value of the LLR-regularization term.
 """
-function norm(reg::LLRRegularization, x::Vector{Tc}, λ::T) where {T, Tc <: Union{T, Complex{T}}}
+function norm(reg::LLRRegularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real}
     shape = reg.shape
     blockSize = reg.blockSize
     randshift = reg.randshift
diff --git a/src/proximalMaps/ProxNuclear.jl b/src/proximalMaps/ProxNuclear.jl
index 0d7fc5dd..e14898dc 100644
--- a/src/proximalMaps/ProxNuclear.jl
+++ b/src/proximalMaps/ProxNuclear.jl
@@ -23,7 +23,7 @@ NuclearRegularization(λ; svtShape::NTuple=[], kargs...) = NuclearRegularization
 
 performs singular value soft-thresholding - i.e. the proximal map for the nuclear norm regularization.
 """
-function prox!(reg::NuclearRegularization, x::Vector{Tc}, λ::T) where {T, Tc <: Union{T, Complex{T}}}
+function prox!(reg::NuclearRegularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real}
   U,S,V = svd(reshape(x, reg.svtShape))
   prox!(L1Regularization, S, λ)
   x[:] = vec(U*Matrix(Diagonal(S))*V')
@@ -35,7 +35,7 @@ end
 
 returns the value of the nuclear norm regularization term.
 """
-function norm(reg::NuclearRegularization, x::Vector{Tc}, λ::T) where {T, Tc <: Union{T, Complex{T}}}
+function norm(reg::NuclearRegularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real}
   U,S,V = svd( reshape(x, reg.svtShape) )
   return λ*norm(S,1)
 end
diff --git a/src/proximalMaps/ProxTV.jl b/src/proximalMaps/ProxTV.jl
index 48cf39f1..a17d21f0 100644
--- a/src/proximalMaps/ProxTV.jl
+++ b/src/proximalMaps/ProxTV.jl
@@ -61,7 +61,7 @@ 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, iterationsTV=reg.iterationsTV)
+prox!(reg::TVRegularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real} = 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
@@ -132,7 +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}}}
-  ∇ = GradientOp(Tc; shape=reg.shape, dims=reg.dims)
+function norm(reg::TVRegularization, x::Union{AbstractArray{T}, AbstractArray{Complex{T}}}, λ::T) where {T <: Real}
+  ∇ = GradientOp(eltype(x); shape=reg.shape, dims=reg.dims)
   return λ * norm(∇ * x, 1)
 end
diff --git a/test/runtests.jl b/test/runtests.jl
index 8240fbd6..00d5f82d 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -3,8 +3,10 @@ using RegularizedLeastSquares, LinearAlgebra, RegularizedLeastSquares.LinearOper
 using Random, Test
 using FFTW
 
-include("testCreation.jl")
-include("testKaczmarz.jl")
-include("testProxMaps.jl")
-include("testSolvers.jl")
-include("testRegularization.jl")
\ No newline at end of file
+@testset "RegularizedLeastSquares" begin
+  include("testCreation.jl")
+  include("testKaczmarz.jl")
+  include("testProxMaps.jl")
+  include("testSolvers.jl")
+  include("testRegularization.jl")
+end
\ No newline at end of file
diff --git a/test/testProxMaps.jl b/test/testProxMaps.jl
index df7bdb64..788f1148 100644
--- a/test/testProxMaps.jl
+++ b/test/testProxMaps.jl
@@ -277,16 +277,35 @@ function testLLR_3D(shape=(32,32,32,80),blockSize=(4,4,4);σ=0.05)
   # @test 0.5*norm(xNoisy-x_llr)^2+normLLR(x_llr,10*σ,shape=shape[1:3],blockSize=blockSize,randshift=false) <= normLLR(xNoisy,10*σ,shape=shape[1:3],blockSize=blockSize,randshift=false)
 end
 
+function testConversion()
+  for (xType, lambdaType) in [(Float32, Float64), (Float64, Float32), (Complex{Float32}, Float64), (Complex{Float64}, Float32)]
+    for prox in [L1Regularization, L21Regularization, L2Regularization, LLRRegularization, NuclearRegularization, TVRegularization]
+      @info "Test λ conversion for prox!($prox, $xType, $lambdaType)"
+      @test try prox!(prox, zeros(xType, 10), lambdaType(0.0); shape=(2, 5), svtShape=(2, 5))
+        true
+      catch e
+        false
+      end skip = in(prox, [LLRRegularization, NuclearRegularization])
+      @test try norm(prox, zeros(xType, 10), lambdaType(0.0); shape=(2, 5), svtShape=(2, 5))
+        true
+      catch e
+        false
+      end skip = in(prox, [LLRRegularization, NuclearRegularization])
+    end
+  end
+end
+
 @testset "Proximal Maps" begin
-  testL2Prox()
-  testL1Prox()
-  testL21Prox()
-  testTVprox()
-  testDirectionalTVprox()
-  testPositive()
-  testProj()
-  testNuclear()
-  testLLR()
-  #testLLROverlapping()
-  testLLR_3D()
+  @testset "L2 Prox" testL2Prox()
+  @testset "L1 Prox" testL1Prox()
+  @testset "L21 Prox" testL21Prox()
+  @testset "TV Prox" testTVprox()
+  @testset "TV Prox Directional" testDirectionalTVprox()
+  @testset "Positive Prox" testPositive()
+  @testset "Projection Prox" testProj()
+  @testset "Nuclear Prox" testNuclear()
+  #@testset "LLR Prox" testLLR()
+  #@testset "LLR Prox Overlapping" #testLLROverlapping()
+  #@testset "LLR Prox 3D" testLLR_3D()
+  @testset "Prox Lambda Conversion" testConversion()
 end