cszn / DnCNN

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
https://cszn.github.io/
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Lower depth yields same performance #95

Open Youyoun opened 2 years ago

Youyoun commented 2 years ago

Hi !

Thanks for publishing your training repository !

I trained the 17 depth model on BSD dataset (train) using the pytorch_training scripts (that I fixed because of some compatibility issues), and it yielded the same result as a model of depth 4 (didn't try to go lower).

It just feels weird that I get the same mean train loss / PSNR with a much lower depth model. I only tried on gray scale images.

Is it possible that something is wrong with the code, or is this result normal ? Is there a thorough study on the performance of DnCNN according to its depth ?

Thank you in advance for your response.