Comments (3)
@SuperCrystal Hi, sorry for the late response. Empirically, testing images with their original size usually delivers better performance than cropping an image patch with 384 x 384 in our experiments.
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@zwx8981
Thanks a lot for your response! Another question is that if std_modeling is False and therefore p = y_diff = y1 - y2 by the code you provided. When I use it in this way, the loss just can not converge. However, if a sigmoid function is used, it can work correctly. Wondering whether you have also test about this. It is really an impressive work anyway :)
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Related Issues (20)
- 请问怎样测试单幅或多幅自己的图像质量? HOT 2
- Input scaling HOT 1
- Could you share me the result from matlab? HOT 3
- About the dataset that is lack of the variance data HOT 2
- About the mean output HOT 1
- about the usage of "detach()" HOT 1
- Output Range for your model? HOT 7
- [Feature requested] Pyhon3 script for generatin a training set HOT 1
- How are the cross-dataset experiments conducted? HOT 1
- About the LIVE dataset
- Model validation HOT 2
- 关于demo.py的测试问题 HOT 1
- Training UNIQUE on single dataset HOT 3
- Negative predicted score HOT 12
- Line70 in DBCNN.py HOT 1
- bilinear pooling layer HOT 1
- Normalization for testing images HOT 1
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- test new dataset HOT 1
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