Mercurial > repos > PredictPDPS / changeset

--- a/src/OpticalFlow.jl	Tue Apr 30 16:00:25 2024 +0300
+++ b/src/OpticalFlow.jl	Thu May 02 21:26:49 2024 +0300
@@ -205,9 +205,23 @@
 end
 
 function activation(x)
-    # return (1/(1 + exp(-(x - 1.5))))                          # Works well with dual norm activation for lighthouse and brainphantom
-    # return 0.2/(1 + exp(-5(x-0.5))) + 0.3                     # Works well with dual norm activation for lighthouse and brainphantom
-    return  1.0 - 0.5/(1+ exp(-100(x-0.7))) 
+    return (1/(1 + exp(-10000(x - 0.05))))  # best for shepp logan
+    # return -abs(x-1)^1/5 + 1     # best for lighthouse and brainphantom    
+    # return x^(5) 
+    # return (1/(1 + exp(-1000(x - 0.075))))  
+    # return 4*(x-0.5)^3 + 0.5  
+    # return (1/(1 + exp(-1000(x - 0.05))))       
+    # return -3(x-0.5)^2 + 0.75    
+    # return (x-1)^51 + 1 
+    # return -abs(x-1)^1/3 + 1
+    # return 16*(x-0.5)^5 + 0.5         
+end
+
+function activationD(x) 
+    # return (1/(1 + exp(-500(x - 0.1))))
+    # return 4*(x-0.5)^3 + 0.5   
+    #return (1/(1 + exp(-1000(x - 0.1))))  
+    return 1.0                  
 end
 
 # Experimental predictor for dual scaling based on activation
@@ -216,15 +230,13 @@
     oldx = copy(x)
     flow!(x, u; threads=(threads==:inner))
     C = similar(y)
-    # cc = abs.(x-oldx)
-    # cm = max(1e-12,maximum(cc))
-    # c = activation.(1 .- cc./ cm)
-    # C[1,:,:] .= c
-    # C[2,:,:] .= c
-    Δx .= activation.((sqrt.(abs.(y[1,:,:]).^2 + abs.(y[2,:,:]).^2))./0.25) 
-    C[1,:,:] .= Δx
-    C[2,:,:] .= Δx
-    y .= C.*y
+    cc = abs.(x-oldx)
+    cm = max(1e-12,maximum(cc))
+    c = activation.(cc ./ cm)
+    C[1,:,:] .= c
+    C[2,:,:] .= c
+    # y .= C.*y
+    y .= y .- 0.75.*C.*y  # 0.75 for brain phantom and shepp logan
 end
 
 function pdflow!(x, Δx, y, Δy, u, :: ZeroDual; threads=:none)
@@ -308,15 +320,18 @@
     Δx = warp(x, tform, axes(x), fillvalue=Flat())
     @. x = Δx
     C = similar(y)
-    # cc = abs.(x-oldx)
-    # cm = max(1e-12,maximum(cc))
-    # c = activation.(1 .- cc./ cm)
-    # C[1,:,:] .= c
-    # C[2,:,:] .= c
-    Δx .= activation.((sqrt.(abs.(y[1,:,:]).^2 + abs.(y[2,:,:]).^2))./0.25) 
-    C[1,:,:] .= Δx
-    C[2,:,:] .= Δx
-    y .= C.*y
+    cc = abs.(x-oldx)
+    cm = max(1e-12,maximum(cc))
+    c = activation.(cc ./ cm)
+    C[1,:,:] .= c
+    C[2,:,:] .= c
+    # Δx .= activation.(sqrt.(abs.(y[1,:,:]).^2 + abs.(y[2,:,:]).^2))./0.25
+    # D = similar(y)
+    # D[1,:,:] .= Δx
+    # D[2,:,:] .= Δx
+    # y .= C.*y
+    # y .= y .- 1.0.*C.*D.*y 
+    y .= y .- 1.0.*C.*y 
 end
 
 # Method for rotation prediction (exploiting property of inverse rotation)

--- a/src/PredictPDPS.jl	Tue Apr 30 16:00:25 2024 +0300
+++ b/src/PredictPDPS.jl	Thu May 02 21:26:49 2024 +0300
@@ -55,7 +55,7 @@
        batchrun_denoising,
        batchrun_predictors,
        demo_denoising1, demo_denoising2, demo_denoising3,
-       demo_denoising4, demo_denoising5, demo_denoising6, demo_denoising7,
+       demo_denoising4, demo_denoising5, demo_denoising6, demo_denoising7, demo_denoising8,
        demo_petS1, demo_petS2, demo_petS3,
        demo_petS4, demo_petS5, demo_petS6, demo_petS7,
        demo_petB1, demo_petB2, demo_petB3,
@@ -222,6 +222,7 @@
 demo_denoising5 = () -> demo(denoising_experiments_pdps_known[5]) # Proximal (old)
 demo_denoising6 = () -> demo(denoising_experiments_pdps_known[6]) # Rotation
 demo_denoising7 = () -> demo(denoising_experiments_pdps_known[7]) # Zero dual
+demo_denoising8 = () -> demo(denoising_experiments_pdps_known[8])
 
 function batchrun_article(kwargs...)
     run_experiments(;experiments=experiments_all,