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unsharp-mask-guided-filtering's Introduction

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Unsharp-mask guided filtering without learning


You can find the code in the folder of "matlab"

Usage


Run "example_smoothing.m", "example_enhancement.m", and "example_flash.m" to get the Figures 3, 4 and 5

Unsharp-mask guided filtering with learning


You can find the code in the folder of "learning"

Requirements

1. CUDA 8.0 and Cudnn 7.5 or higher
2. GPU memory 10GB or higher
3. Python 2.7 or higher
4. Tensorflow 2.0 or higher. If your Tensorflow version is lower than 2.0, you should replace "import tensorflow.compat.v1 as tf" with "import tensorflow as tf" in "main.py", "model.py", and "ops.py"

Training

1. Prepare your data (download the NYU Depth V2 dataset here) following Section V-A.
2. Set the experiment settings in ¨tr_param.ini¨ in which phase = train, and set other parameters accordingly (refer to our paper).
3. Run ¨python main.py¨

Testing

1. Prepare your data following Section V-A.
2. Set the experiment settings in ¨tr_param.ini¨ in which phase = test, and set other parameters accordingly (refer to our paper).
3. Run ¨python main.py¨

Please cite our paper when you use this code.

 @article{shi2021unsharp,
    title={Unsharp Mask Guided Filtering},
    author={Shi, Zenglin and Chen, Yunlu and Gavves, Efstratios and Mettes, Pascal and Snoek, Cees GM},
    journal={IEEE Transactions on Image Processing},
    volume={30},
    pages={7472 - 7485},
    year={2021},
    publisher={IEEE}
 }

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unsharp-mask-guided-filtering's Issues

training error

hello! when I try to use learning->training->denoising->main.py to train the net, I find it will report an error when running the "self.pred_depth_list = self.umgf_model(self.input_guide, self.input_target,self.niters)" in "self.build_model()" during the intial process. As the input shape is set as "shape=[None, None, None, self.inputI_chn]", it will make the "N = box_filter_op(tf.ones((1, 1, x_shape[2], x_shape[3]), dtype=x.dtype), r)" failed for the "x_shape[2]" shouldn't be nonetype. Could you plz help me out? thanks a lot!

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