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Official implementation of the paper "CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image" (CVPR 2022)

Results 15 CVF-SID_PyTorch issues
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Dear Author:I am not able to get the DND benchmark dataset at the moment. Can you please share the dnd benchmark dataset zip package with me?

PS E:\githubProjects\CVF-SID_PyTorch\src> python test.py --device 0 --config ../models/CVF_SID/SIDD_Val/config.json --resume ../models/CVF_SID/SIDD_Val/model_best.pth Traceback (most recent call last): File "test.py", line 11, in from torch.distributed.optim import DistributedOptimizer File "D:\python3.8\lib\site-packages\torch\distributed\optim\__init__.py", line 8, in from...

Hello, I want to know how to modify the test code to realize my demand, produce the denoised images. Could you tell me the solution? thx.

`self.train_metrics = MetricTracker('Total_loss', writer=self.writer)` I am puzzled by how to maintain the values of self.train_metrics in trainer.py? I did not find the implementation of the self.train_metrics.update() in your coding

Hello! Thank you for your amazing job! Could you release your detail information about the processing of SIDD-Medium Dataset? There are only 320 noisy images in this dataset, so did...

Hello, I would like to know if the noise is not zero-mean, if only part of the code needs to be modified to converge or the method can only be...

Thanks for sharing the source code and the pre-trained model on SIDD validation dataset. I tested the provided pre-trained model on SIDD validation dataset and only got 34.14/0.855 (SPNR/SSIM). ![image](https://user-images.githubusercontent.com/48625631/219932655-ac50bef5-8f90-42e6-b253-ab435212abab.png)...

HI!If I want to train on our own dataset,and how should I prepare data, such as ‘Scene_Instances.txt’

Hello,I would like to ask,what is the matrix c below denote? ![image](https://user-images.githubusercontent.com/58362707/212523497-5c9c11dd-d32b-47d2-9711-9754ac1b13d3.png) It would be appreciate if you could explain the meaning of std below: ![image](https://user-images.githubusercontent.com/58362707/212523591-1323e168-a2ed-4949-bd05-5a07300eadbe.png)