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7_SLM_Fraunhofer.jl
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### A Pluto.jl notebook ###
# v0.19.41
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
el
end
end
# ╔═╡ 9e6d909f-06de-46e7-929a-61bfd67bea26
using Zygote, WaveOpticsPropagation, FFTW, ImageShow, FiniteDifferences, CUDA, Optim, IndexFunArrays, FourierTools, TestImages, NDTools
# ╔═╡ 4ebbfa83-442c-4e3b-8461-a608de72c66f
using Noise
# ╔═╡ ef79854f-0ae9-41aa-8f92-aae8d7023d71
using Plots
# ╔═╡ f9a13d72-f904-467c-8346-a9ef272693ff
using PlutoUI
# ╔═╡ a4555452-3963-436f-8e1a-5f9d6ad733f8
using Statistics
# ╔═╡ 34dc58b0-9d8f-4c24-a9f7-1de26ebfc5e0
md"# Load Packages"
# ╔═╡ 20c5faa9-5694-42fd-b1bb-0c5093f6c93c
begin
# use CUDA if functional
use_CUDA = Ref(true && CUDA.functional())
var"@mytime" = use_CUDA[] ? CUDA.var"@time" : var"@time"
togoc(x) = use_CUDA[] ? CuArray(x) : x
end
# ╔═╡ b8cf117e-d084-4d42-8022-03fd6ac4c417
TableOfContents()
# ╔═╡ fd147f54-dbf9-4a2f-8c17-938566780c5c
md"# Create a beam"
# ╔═╡ a971769f-1215-4422-a209-fad710be16dd
sz = (1080, 1080)
# ╔═╡ bdf3b801-28e0-41cf-9dc4-92fe8fdaca0e
beam = togoc(0im .+ gaussian(Float32, sz, sigma=(100, 150))) .* togoc(cis.(conv((normal(Float32, (sz), sigma=7)), 100 .* rand(Float32, (sz)))));
# ╔═╡ 36b798c9-feee-43b7-b9ec-4f0558a44d2c
simshow(Array(beam))
# ╔═╡ ad4ef631-841d-412e-a09a-4db60fa7e4a9
z = 150f-3
# ╔═╡ 15011619-f26a-4858-b0a0-d5dbcad49824
λ = 633f-9
# ╔═╡ 48048a82-5bbf-4f16-a7d9-15993c83f905
L = 8.64f-3
# ╔═╡ 6280a8d5-be93-483b-9b40-725f3e52424b
L ./ sz
# ╔═╡ db1724ea-96f1-445a-9cd8-e3f5dfab57f9
λ * sz[1] * z / L / sz[1]
# ╔═╡ 2dd88724-a5eb-4ffe-97a5-30e207959220
md" The field size after propagation is going to be"
# ╔═╡ 3f791571-2a36-4479-a853-c6ed4e8a270d
md"# Forward model
As forward model, we use different defocus patterns and diffuser noise
"
# ╔═╡ e52cfcd8-eec8-4a8b-b953-f1c6ef2be959
N = 20
# ╔═╡ 42c00ce2-d1e4-4f84-84be-a7e4cd6551ce
begin
diffuser = conv((normal(Float32, (sz), sigma=4)), 100 .* rand(Float32, (sz..., N)))
diffuser_c = 0im .+ togoc(diffuser)# .* box(sz, (120, 150))
diffuser_c2 = CUDA.copy(diffuser_c)
diffuser_c .= cis.(diffuser_c)
CUDA.@sync diffuser_c[:, :, 1] .= cis.(togoc(0.3 .* rr2(sz, scale=0.05)) .+ 0.4f0 .* diffuser_c2[:, :, 1])
CUDA.@sync diffuser_c[:, :, 2] .= cis.(togoc(0.2 .*rr2(sz, scale=0.05)) .+ 0.4f0 .* diffuser_c2[:, :, 2])
CUDA.@sync diffuser_c[:, :, 3] .= cis.(togoc(0.1 .*rr2(sz, scale=0.05)) .+ 0.4f0 .* diffuser_c2[:, :, 3])
CUDA.@sync diffuser_c[:, :, 4] .= cis.(togoc(0.3 .*rr2(sz, scale=0.05)) .+ 1.0f0 .* diffuser_c2[:, :, 4])
end;
# ╔═╡ d058c9ba-82ec-4f1e-8938-eb9e1e95b713
fraunhofer = Fraunhofer(beam .* diffuser_c, z, λ, L)
# ╔═╡ 30eef203-9725-434b-b3e5-99df65c5d113
fraunhofer.params.Lp ./ sz[1]
# ╔═╡ 5d21e0bc-ea6e-4b2c-8977-d1761d1b3c65
fraunhofer(beam .* diffuser_c) ./ fftshift(fft(ifftshift(beam .* diffuser_c, (1,2)), (1,2)), (1,2));
# ╔═╡ df72f725-8464-46b0-a291-8503ea4e563f
size(diffuser_c)
# ╔═╡ 5adae7a1-9efa-49d9-8889-9b6d2aa13bf0
simshow(Array(cis.(diffuser_c2[:, :, 2])))
# ╔═╡ 3fa2e0db-847b-4392-b297-52038f201f41
@bind iz2 Slider(1:size(diffuser_c, 3))
# ╔═╡ f98d20f2-70ec-490f-99a2-a3f90e115a53
simshow(Array(diffuser_c[:, :, iz2]))
# ╔═╡ 90d78713-0d4d-48b2-b590-a79d4e7c12d5
simshow(Array(diffuser_c[:, :, iz2]))
# ╔═╡ f9c4d5ca-a35a-462a-b5f4-4120012f5a1b
fwd(x) = begin
return abs2.(fraunhofer(x .* diffuser_c))
end
# ╔═╡ d84457d3-30ae-4711-b47f-7d2a7376a237
md"# Set up Optimization"
# ╔═╡ d219be8b-2d22-4fbe-b022-217edfeec5dc
function make_fg!(fwd, measured, loss=:L2)
L2 = let measured=measured, fwd=fwd
function L2(x)
return sum(abs2, fwd(x) .- measured)
end
end
anscombe = let measured=measured, fwd=fwd
function anscombe(x)
return sum(abs2, 2 .* sqrt.(fwd(x) .+ 3f0 / 8) .- 2 .* sqrt.(measured .+ 3f0 ./ 8))
end
end
f = let
if loss == :L2
L2
elseif loss == :anscombe
anscombe
end
end
g! = let f=f
function g!(G, rec)
if !isnothing(G)
return G .= Zygote.gradient(f, rec)[1]
end
end
end
return f, g!
end
# ╔═╡ 692e0263-8273-45a6-b3e6-c30bbeaeb649
begin
measurement_c = fwd(beam)
measurement_c .= togoc(poisson(Array(measurement_c), 50_000))
measurement_c .= togoc(quantization(Array(measurement_c), 256, maxv=1.5 * maximum(measurement_c)))
end;
# ╔═╡ 7ea48e4f-ad35-4ed6-9367-64ce5d1f0ca4
quantization([0.0, 13, 30], 10)
# ╔═╡ 0310a538-c82d-412d-9f60-576c416b17dd
extrema(measurement_c)
# ╔═╡ 5c550775-a614-44dc-a81d-638448d23067
@bind iz Slider(1:N)
# ╔═╡ 56a03baf-3873-43d5-b8c1-8569de6026b0
simshow(Array(measurement_c)[:, :, iz], γ=1, cmap=:turbo)
# ╔═╡ 25627a6d-c2f4-43bc-bf4a-325024b2db8c
maximum(measurement_c)
# ╔═╡ de16936c-7774-4aab-9702-f0cf4fa660fd
md"# Optimize!"
# ╔═╡ ee5260f3-d3a4-4bf2-8b54-31723c3c516f
rec0 = togoc(ones(ComplexF32, sz));
# ╔═╡ d90bff7e-150d-407d-a510-385fd5a32df3
f, g! = make_fg!(fwd, measurement_c, :anscombe)
# ╔═╡ 05b63595-61dd-416f-8610-0f6ee1919a6f
@mytime res = Optim.optimize(f, g!, rec0, LBFGS(),
Optim.Options(iterations = 25,
store_trace=true))
# ╔═╡ ec93edb8-5877-4de2-95d9-6bf85ad135a0
md"# Analyze Results"
# ╔═╡ 9ed4c43f-1ad5-43b1-bef6-40faf027207d
plot([t.iteration for t in res.trace], [t.value for t in res.trace], label="LBFGS", yaxis=:log, ylabel="loss", xlabel="iteration")
# ╔═╡ 6c43635a-dac1-48c8-9736-0e8e66ab625e
sum(abs2, measurement_c .- fwd(res.minimizer))
# ╔═╡ cf15e57a-9c3d-4751-bace-68611ff602ba
bb = beam ./ sum(select_region((beam), M = 1/8));
# ╔═╡ 150679b3-79f6-426b-99ca-8bc179877af6
rm = res.minimizer ./ sum(select_region((res.minimizer), M = 1/8));
# ╔═╡ 5372b296-75d6-497d-b6c9-e2f104e76522
[simshow(Array(rm), γ=0.5) simshow(Array(bb), γ=0.5)]
# ╔═╡ 1d7cda5a-01a8-4bf7-a744-ce9df9f4ab04
@bind i4 Slider(1:2)
# ╔═╡ 82273df3-554e-43f9-a881-63609c239648
cat(simshow(Array(rm), γ=0.1), simshow(Array(bb), γ=0.1), dims=3)[:, :, i4]
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
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NDTools = "98581153-e998-4eef-8d0d-5ec2c052313d"
Noise = "81d43f40-5267-43b7-ae1c-8b967f377efa"
Optim = "429524aa-4258-5aef-a3af-852621145aeb"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
WaveOpticsPropagation = "c4c7a1f9-3adc-4a73-843a-d378b6c86436"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
[compat]
CUDA = "~5.3.1"
FFTW = "~1.8.0"
FiniteDifferences = "~0.12.31"
FourierTools = "~0.4.3"
ImageShow = "~0.3.8"
IndexFunArrays = "~0.2.7"
NDTools = "~0.6.0"
Noise = "~0.3.3"
Optim = "~1.9.4"
Plots = "~1.40.4"
PlutoUI = "~0.7.59"
TestImages = "~1.8.0"
WaveOpticsPropagation = "~0.3.0"
Zygote = "~0.6.69"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
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