I just released a pytorch implementation of the HDMI denoising method that can be used on GPU: github.com/ahoudard/HDMI
This version allows to run the EM algorithm and the denoising in about half a minute!
python run_HDMI_denoising.py alley.png --stdv 0.1 --n_iter 40 --verbose --gpu
selected device: cuda
run EM algorithm...
denoise patches...
total runtime: 34s
PSNR: 29.8940dB
With the publication in the SSVM 2021 proceeding of our paper Wasserstein Generative Models for Patch-based Texture Synthesis we just released the corresponding Python code available on github !
Example on my local laptop (core i5 CPU) :
python run_optim_synthesis.py radishes.png --save
cpu
iteration 0 - elapsed 2s - loss = 3.3926462531089783
iteration 50 - elapsed 131s - loss = 0.5145381316542625
iteration 100 - elapsed 260s - loss = 0.23772023245692253
iteration 150 - elapsed 390s - loss = 0.20723320543766022
iteration 200 - elapsed 522s - loss = 0.20013415440917015
iteration 250 - elapsed 652s - loss = 0.19069230556488037
iteration 300 - elapsed 781s - loss = 0.18442947790026665
iteration 350 - elapsed 912s - loss = 0.18047476187348366
iteration 400 - elapsed 1042s - loss = 0.17874254286289215
iteration 450 - elapsed 1162s - loss = 0.17782559990882874
DONE - total time is 1292s
