Hi there. Firstly thank you for the very interesting paper! I am very interested in modeling noise profiles with your program. I have trained the model using the default settings, with DND images denoised using a DNCNN network. However, the dataset generated (using DIV2K) does not seem to have noise similar to the original DND images. There seems to be some incorrect colour shifts as well as some checkerboard artefacts.

Did you observe this in your work and have you any advice for me with regards to settings I could change? Does training for longer help? This seems to be a problem in training PseudoISP as my training data appears to be good. I will try to train again. For now below is an example of the images produced.

Checkerboarding

Colour artefacts

Strange colour with Bayer/checkerboard artefacts?

I did try to train DNCNN again using this but it made performance worse. Any suggestions are welcome

I do not know why but I could not run Stage 2 train, specifically 'Generate_Synthesis_Dataset()'.

I solved this by adding a 'train' flag to the three models and disabling weight initialization when the train flag was false. Without this the program randomly crashes and needs to be killed in htop/nvtop.

Simply as follows:

class RGB2PACK(nn.Module):
    def __init__(self, channels=3, filters_num = 128, filters_pack = 4, train=True):
        super(RGB2PACK, self).__init__()

        # RGB2RAW Network
        self.RGB2RAW = nn.Sequential(
            nn.Conv2d(channels, filters_num, 3, 1, 1),
            nn.ReLU(True),
            nn.Conv2d(filters_num, filters_num, 3, 1, 1),
            nn.ReLU(True),
            nn.Conv2d(filters_num, filters_num, 3, 1, 1),
            nn.ReLU(True),
            nn.Conv2d(filters_num, filters_num, 3, 1, 1),
            nn.ReLU(True),
            nn.Conv2d(filters_num, filters_num, 3, 1, 1),
            nn.ReLU(True),
            nn.Conv2d(filters_num, channels, 3, 1, 1),
            nn.ReLU(True))

        # Mosaic
        self.mosaic = Mosaic_Operation()
        if train:
            self._initialize_weights()

Repeat for the other two networks.

Three questions.

1/ According to your paper, you perform evalutation on SIDD benchmark and DND benchmark. I saw results about the PSNR on two datasets. Can you provide both the PSNR and SSIM on two datasets? I only find results on sRGB of SIDD on that website. I need specific results for comparison.

2/ There are two results of PseudoISP on DND leaderboard. It seems that the second one is illustrated on the paper. What do the two methods represent?

3/ From my opinion, PseudoISP also need extra data for training, just like CycleISP, but not the same.

Thanks a lot! Im looking forward to your replies.

Hi,

Thank you for this wonderful work. I m hoping to use this for my project since I do not have the ground truth for the set of ultrasound medical images I am working with. I would really appreciate it if you can share a quick demo of how I can use the pre-trained denoising models along with the Pseudo-isp to generate clean data for the medical images I have. I am attaching a sample image to this post:

I really appreciate your work. Thanks in advance!

Yusuf