Comments (6)
I would also add performance for lower Noise STD (5, 10).
How does it perform for real world images?
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The average PSNR(dB) results of CBM3D and DnCNN on (color) BSD68 dataset.
Noise Level | 5 | 10 | 15 | 25 | 35 | 50 |
---|---|---|---|---|---|---|
CBM3D | 40.24 | 35.91 | 33.52 | 30.71 | 28.89 | 27.38 |
DnCNN | 40.50 | 36.31 | 33.99 | 31.31 | 29.66 | 28.01 |
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What about real world performance?
Does it do a decent job on real world noise?
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@RoyiAvital Here are real noisy images. It performs well if you select the model with proper noise level.
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Hi,
It would be great if there are Before / After on real world images (For various choice of the noise level).
Thank You.
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@RoyiAvital This is one of my ongoing researches.
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Related Issues (20)
- The Wrong when I using the \DnCNN\TrainingCodes\DnCNN_TrainingCodes_v1.1\Demo_Train_model_64_25_Res_Bnorm_Adam.m
- 提供训练集
- params_updates中adam算法的参数更新好像有误
- Size of input images
- Version of DnCNN that is compatible with tensorflow 2.0
- How to resolve this bug? could you please tell me?maybe I solved above problem,but I met the follow problem :
- bug in usage of OpenCV resize function
- Batch Normalization gives worse perfromence
- ERROR: Could not find a version that satisfies the requirement torch==0.4.1 ERROR: No matching distribution found for torch==0.4.1 HOT 1
- Data Preparation for DnCNN3
- vl_argparse(opts, varargin);No function or variable is defined—— 'vl_argparse'。And I can't find matlab code about this function
- Are there any files missing?Such as vl_simplenn_move.m and vl_argparse.m
- main_test.py
- Sigma=45 model in .h5
- If I want to input my own image for deep learning,but i don't need add to Gassian noise level,what should I do? HOT 1
- Lower depth yields same performance
- Repetitive Dataset and DataLoader Creation in PyTorch version
- About Testing
- About image shape debug HOT 2
- The wrng
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