scripts/run.jl

@@ -19,20 +19,27 @@ ctx(experiment_convergence_rate, "fem/convergence/rate")
 # pd est, L1-TV, bad
 
 
-# applications
+# misc experiments
 
-# denoising
-ctx(experiment_denoise, "fem/denoise")
-# inpainting
+# inpainting (OLD)
 ctx(experiment_inpaint, "fem/inpaint")
 # adaptive optical flow
 ctx(experiment_optflow_middlebury_all, "fem/optflow/middlebury")
 
-# additional experiments for paper
-
-# combined inpainting + denoising with mixed noise
-ctx(experiment_inpaint_denoise, "fem/optflow/inpaint_denoise")
-
-# comparison: warping and adaptivity
-ctx(experiment_optflow_middlebury_warping_comparison, "fem/optflow/middlebury_warping_comparison")
-
+function paper1(ctx)
+    # newton vs other algs
+    ctx(experiment_convergence_rate, "fem/convergence/rate")
+    # denoising
+    ctx(experiment_denoise, "fem/denoise")
+    # combined inpainting + denoising with mixed noise
+    ctx(experiment_inpaint_denoise, "fem/inpaint_denoise")
+
+    # comparison: warping and adaptivity
+    ctx(experiment_optflow_middlebury_warping_comparison, "fem/opticalflow/middlebury_warping_comparison")
+end
+
+function paper2(ctx)
+    # comparison: warping and adaptivity
+    # TODO: adaptivity parameter?
+    ctx(experiment_optflow_middlebury_warping_comparison, "fem/opticalflow/middlebury_warping_comparison")
+end

scripts/run_experiments.jl

@@ -988,10 +988,12 @@ function denoise_pd(ctx)
 
     # semi-implicit primal dual parameters
     mu = st.alpha2 + st.beta # T = I, S = I
-    mu /= 100 # kind of arbitrary?
+    #mu /= 100 # kind of arbitrary?
 
-    tau = 1e-1
     L = norm_gradient(mesh)
+    #tau = 1e-1
+    tau = 1 / L
+    #L = 100
     sigma = inv(tau * L^2)
     theta = NaN # initialized later
 
@@ -1022,6 +1024,7 @@ function denoise_pd(ctx)
         while true
             k += 1
             if ctx.params.algorithm == :pd1
+                theta = 1 / sqrt(1 + 2 * mu * tau) # constant
                 # no step size control
                 step_pd2!(st; sigma, tau, theta)
             elseif ctx.params.algorithm == :pd2
@@ -1322,6 +1325,7 @@ function experiment_approximation(ctx)
 end
 
 # TODO: deduplicate, cf. optflow()
+# NOTE: used also by inpaint_denoise experiment
 function inpaint(ctx)
     # expect ctx.params.g_arr
 
@@ -1350,13 +1354,15 @@ function inpaint(ctx)
         if ctx.params.n_refine == 0
             # if we use a grid at image resolution, we wan't to avoid
             # information loss at all cost.
-            # projet_l2_pixel is not suited for inpainting as it is a global
+            # project_l2_pixel is not suited for inpainting as it is a global
             # operator, which may leak data from the inpainting domain
             interpolate!(st.g, x -> evaluate_bilinear(g_arr, x))
+            interpolate!(st.tdata, x -> evaluate_bilinear(mask_arr, x))
         else
             project_image!(st.g, g_arr)
+            # TODO: maybe a more conservative choice would be good
+            project_image!(st.tdata, mask_arr)
         end
-        project_image!(st.tdata, mask_arr)
     end
 
     save_step(i) =
@@ -1427,7 +1433,7 @@ function inpaint(ctx)
     u_sampled = sample(st.u)
     saveimg(joinpath(ctx.outdir, "g.png"), grayclamp.(to_img(g_arr)))
     saveimg(joinpath(ctx.outdir, "output.png"), grayclamp.(to_img(u_sampled)))
-    save_csv(joinpath(ctx.outdir, "mesh.csv"), st.u)
+    #save_csv(joinpath(ctx.outdir, "mesh.csv"), st.u)
     savedata(joinpath(ctx.outdir, "data.tex");
         ctx.params.eps_newton, ctx.params.n_refine,
         st.alpha1, st.alpha2, st.lambda, st.beta, st.gamma1, st.gamma2,
@@ -1495,7 +1501,12 @@ function optflow(ctx)
     size(ctx.params.imgf0) == size(ctx.params.imgf1) ||
 	throw(ArgumentError("non-matching image sizes"))
 
-    project_image! = project_l2_lagrange!
+    #project_image! = project_l2_lagrange!
+    project_image!(f, img) =
+        ctx.params.n_refine == 0 ?
+            interpolate!(f, x -> evaluate_bilinear(img, x)) : # fine mesh
+            project_l2_lagrange! # coarse adaptive mesh
+
     eps_newton = 1e-3 # cauchy criterion for inner newton loop
     eps_warp = 0.05
 
@@ -1672,6 +1683,13 @@ function experiment_optflow_middlebury_warping_comparison(ctx)
         warp = false, refine = false, n_refine = 0)
     ctx(experiment_optflow_middlebury_all_benchmarks, "warping";
         warp = true, refine = false, n_refine = 0)
+end
+
+function experiment_optflow_middlebury_warping_comparison_adaptive(ctx)
+    ctx(experiment_optflow_middlebury_all_benchmarks, "vanilla";
+        warp = false, refine = false, n_refine = 0)
+    ctx(experiment_optflow_middlebury_all_benchmarks, "warping";
+        warp = true, refine = false, n_refine = 0)
     ctx(experiment_optflow_middlebury_all_benchmarks, "adaptive-warping";
         warp = true, refine = true, n_refine = 6)
 end