ViDeNN
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The denoising effect is abnormal
when run ‘python main_ViDeNN.py --use_gpu=1 --checkpoint_dir=ckpt_videnn --save_dir='./test_data/result/frame1_videnn' --test_dir='./test_data/test/frame1'’,the image after denoising is abnormal.
original image
denoised iamge
excuse me, what is the reason?
when run ‘python main_ViDeNN.py --use_gpu=1 --checkpoint_dir=ckpt_videnn --save_dir='./test_data/result/frame1_videnn' --test_dir='./test_data/test/frame1'’,the image after denoising is abnormal. original image
denoised iamge
excuse me, what is the reason?
还好看到你的结果,不然我也就花时间复现效果了
I‘ve got even worse results than you,I will give up this work (T_T)
when run ‘python main_ViDeNN.py --use_gpu=1 --checkpoint_dir=ckpt_videnn --save_dir='./test_data/result/frame1_videnn' --test_dir='./test_data/test/frame1'’,the image after denoising is abnormal. original image
denoised iamge
excuse me, what is the reason?
I've got the normal results by input the dark-light case image
The denoising is excellent, however I am getting the same issues as above. How can I resolve this?
This issue has been reported already several times: https://github.com/clausmichele/ViDeNN/issues/1 https://github.com/clausmichele/ViDeNN/issues/7 https://github.com/clausmichele/ViDeNN/issues/8 https://github.com/clausmichele/ViDeNN/issues/20
A possible solution is to use a different checkpoint as explained here: https://github.com/clausmichele/ViDeNN/issues/1#issuecomment-442762424
Another solution would be to retrain the network from scratch.
The other checkpoint was better but still had artifacts. I'm retraining the network now. If It produces better results I can post the checkpoint if you like.
retrained and get same results. I wonder if it needs different noise generation? What do you suggest?