using NtToolBox
using PyPlot

xsharp = load_image("NtToolBox/src/data/lena.png")*256;

figure(figsize=(9,9))
imageplot(xsharp)
title("This is Lena");

(N1,N2) = size(xsharp)
noiselevel = 20
y = xsharp + noiselevel * randn(N1,N2);

figure(figsize=(9,9))
imageplot(y)
title("Noisy version of Lena");

D = x -> cat(3,[diff(x,1); zeros(1,size(x,2))], [diff(x,2) zeros(size(x,1),1)]);

Dadj = v -> [-v[1,:,1]'; -diff(v[1:end-1,:,1],1); v[end-2,:,1]'] + 
            [-v[:,1,2] -diff(v[:,1:end-1,2],2)  v[:,end-2,2]];

function grad_f_conj(x,y)
    return x + y
end;

function prox_g_conj(u, Lambda)
    return u./repeat(max(sqrt(sum(u.^2,3))/Lambda,1),outer=(1,1,2)) # soft-thresholding
end;

include("NtSolutions/optim_3_condat_fb_duality/exo1.jl");

figure(figsize=(9,9))
imageplot(xdenoised)
title("Denoised image");

figure(figsize=(15,7))
plot(Ep_array)
plot(-Ed_array)
xlim(0,nbiter);

figure(figsize=(15,7))
plot(log10((Ep_array+Ed_array)))
xlim(0,360);

include("NtSolutions/optim_3_condat_fb_duality/exo2.jl");

figure(figsize=(15,7))
plot(log10((Ep_array+Ed_array)))
plot(log10((Ep_array_fista+Ed_array_fista)))
xlim(0,nbiter);
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