DnCNN-tensorflow
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wbhu
:octocat::octocat:A tensorflow implement of the paper "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"
This code is poorly written and can be tuned, but memory will continue to increase and will not be released until the memory explodes.
Hello! I've found a performance issue in /model.py: `.batch(batch_size)`[(here)](https://github.com/wbhu/DnCNN-tensorflow/blob/3c113f5620bb5abe45e03f07ae27e9dbe6ec1a79/model.py#L218) should be called before `.map(im_read, num_parallel_calls=num_parallel_calls)`[(here)](https://github.com/wbhu/DnCNN-tensorflow/blob/3c113f5620bb5abe45e03f07ae27e9dbe6ec1a79/model.py#L216) and `.map(get_patches_fn, num_parallel_calls=num_parallel_calls)`[(here)](https://github.com/wbhu/DnCNN-tensorflow/blob/3c113f5620bb5abe45e03f07ae27e9dbe6ec1a79/model.py#L217), which could make your program more efficient. Here is [the tensorflow document](https://tensorflow.google.cn/guide/data_performance?hl=zh_cn#vectorized_mapping)...
While executing session.run I got the following error, I am using Python 3, please help me asap. This is where it is called: File model.py line 139: _, loss, summary...
It seems there is a non-implemented argument named temporal which is used for test and does not do anything. It should be removed
Hi Wenbo, thank you for sharing the code. Could you tell me how you trained the model in the checkpoint_demo folder, such as the training dataset and parameters? I tried...
 After test, my image was still with noise the same as original files. Was there any problems with the code? And how to figure out the clear picture?
