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View Code? Open in Web Editor NEWImage denoising using deep CNN with batch renormalization(Neural Networks,2020)
Home Page: https://www.sciencedirect.com/science/article/pii/S0893608019302394
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cqduan hefv57 templeblock pzw520125 yuqili2019 mayinjin liuwenhaha zoq jacksky64 chw0806-github annaloua wangweiwei104 kevinkevin325 mzahirulj youtang1993 ouisyasser cv-ip adnankhalid7454 kesavsivakumar yang-fei zongjg arthasjax riceshrimp michelle-fridlund anparashar jnpn7 rexche mosedo peng-pengpeng thw1023 samyukthaibrdnet's Issues
Some minor changes
Thank you for your code.
I just found a small problem.The Readme says requiring keras 2.0, but in keras 2.0 there is no Subtract and the code could not run on my device.
I change keras to 2.1.0 and make some small changes then the code worked, I suggest the requirements should be changed.
real noisy images train code
田教授您好，请问您训练真实噪声图像的代码链接是这个吗？http://www.yongxu.org/lunwen.html，这个网站显示已经被拦截。可否将代码分享至百度网盘？
ZeroDivisionError: division by zero --- What is the reason?
mish with brdnet
Hi, helloxiaotian,
It is more question or suggestion rather than a bug:
Did you try your net with Mish activation function (https://github.com/digantamisra98/Mish)?
As per paper and repo- it is better than Relu.
Another question:
which tf version are you using?
I am struggling with batch_renorm - I can not make it work with tf2.0
Cheers,
senovr
ValueError
Hello,
I got this error after executing the code,
Thank you for your help.
Exception has occurred: ValueError
in user code:
D:\Python37\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self, iterator)
e:\BRDNet\BRDNet12_2\BRDNet12\grayimage\batch_renorm.py:180 call *
self.add_update([K.update(self.r_max, r_val),
D:\Python37\lib\site-packages\tensorflow\python\keras\backend.py:959 variable **
constraint=constraint)
D:\Python37\lib\site-packages\tensorflow\python\ops\variables.py:262 call
return cls._variable_v2_call(*args, **kwargs)
D:\Python37\lib\site-packages\tensorflow\python\ops\variables.py:256 _variable_v2_call
shape=shape)
D:\Python37\lib\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
D:\Python37\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2857 creator
return next_creator(**kwargs)
D:\Python37\lib\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
D:\Python37\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2857 creator
return next_creator(**kwargs)
D:\Python37\lib\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
D:\Python37\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2857 creator
return next_creator(**kwargs)
D:\Python37\lib\site-packages\tensorflow\python\ops\variables.py:67 getter
return captured_getter(captured_previous, **kwargs)
D:\Python37\lib\site-packages\tensorflow\python\eager\def_function.py:702 invalid_creator_scope
"tf.function-decorated function tried to create "
ValueError: tf.function-decorated function tried to create variables on non-first call.
File "E:\BRDNet\BRDNet12_2\BRDNet12\grayimage\mainimprovement.py", line 130, in train
callbacks=[ckpt, csv_logger, lr])
File "E:\BRDNet\BRDNet12_2\BRDNet12\grayimage\mainimprovement.py", line 187, in
model = train()
software compatiblity
hi i am tring to run your code in pycharm but there is many error as you use ubuntu so kindly tell me is this working on window and if yes then in which software we have to use it
describe method
parser.add_argument('--model', default='BRDNet', type=str, help='choose a type of model')
in this line can we change method from change BRDNet to DnCNN[60]?or any other method that you give the value in result
关于文本去噪效果
您好， x 是脏图片， 标答是干净图片，训练完了，psnr30, 但是图片并没有去噪效果，请问这是什么问题呢。
batch_renorm exists some errors
TypeError: add_weight() got multiple values for argument 'name'
this is about the version of tensorflow or keras?
ValueError
in grayimage/mainimprovement.py , the following error occurs..
get_batch_y = get_batch_y.astype('float32')/255.0
ValueError: setting an array element with a sequence.
I tried printing out 'get_batch_y' and found that its getting only the batch size and no other information.
get_batch_y.shape = (20,)
type(get_batch_y) = np.array
Do you know any possible solution for these problems?
How to test the BRDnet on natural image with noise？
Hello！I have test the BRDnet on provided test set, I found that the BRDnet add the noise on the clear image based the on a fixed sigma and then remove the noise, it works really well. However, if I want to remove the noise of a daily image that I don't know its noise sigma, what should I do with BRDnet?
I try to set the --sigma = 0 and try the model of different levels, and the test results are much worse than the results on the given-sigma-noise image. I think that this network is not practical if it could only work on the given-sigma-noise images.
Thanks anyway
Cuda usage
hello sir , could you tell me where you used cuda used in your programme or cuda is just requirment of system to run the code and if it is so then tell me can this programme run on gpu ?
The links for data are not working
The links given for training data are not working.
real noisy image datasets
真实噪声图像训练数据集和训练代码呢？
real noisy images train code
您好 您分享的real noisy images denosing code 又失效了 请问能否再提供一个新的链接 非常感谢！
load model error!
I've tried test mainimprovementcolor.py but in line 158 I got this error:
tracking <tf.Variable 'batch_renormalization_2/batch_renormalization_2_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_2/batch_renormalization_2_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_2/batch_renormalization_2_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_3/batch_renormalization_3_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_3/batch_renormalization_3_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_3/batch_renormalization_3_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_4/batch_renormalization_4_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_4/batch_renormalization_4_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_4/batch_renormalization_4_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_5/batch_renormalization_5_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_5/batch_renormalization_5_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_5/batch_renormalization_5_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_17/batch_renormalization_17_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_17/batch_renormalization_17_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_17/batch_renormalization_17_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_6/batch_renormalization_6_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_6/batch_renormalization_6_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_6/batch_renormalization_6_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_7/batch_renormalization_7_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_7/batch_renormalization_7_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_7/batch_renormalization_7_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_8/batch_renormalization_8_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_8/batch_renormalization_8_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_8/batch_renormalization_8_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_9/batch_renormalization_9_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_9/batch_renormalization_9_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_9/batch_renormalization_9_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_10/batch_renormalization_10_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_10/batch_renormalization_10_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_10/batch_renormalization_10_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_11/batch_renormalization_11_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_11/batch_renormalization_11_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_11/batch_renormalization_11_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_18/batch_renormalization_18_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_18/batch_renormalization_18_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_18/batch_renormalization_18_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_12/batch_renormalization_12_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_12/batch_renormalization_12_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_12/batch_renormalization_12_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_13/batch_renormalization_13_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_13/batch_renormalization_13_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_13/batch_renormalization_13_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_14/batch_renormalization_14_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_14/batch_renormalization_14_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_14/batch_renormalization_14_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_15/batch_renormalization_15_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_15/batch_renormalization_15_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_15/batch_renormalization_15_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_16/batch_renormalization_16_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_16/batch_renormalization_16_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_16/batch_renormalization_16_t:0' shape=(1,) dtype=float32> t
tracking <tf.Variable 'batch_renormalization_19/batch_renormalization_19_r_max:0' shape=(1,) dtype=float32> r_max
tracking <tf.Variable 'batch_renormalization_19/batch_renormalization_19_d_max:0' shape=(1,) dtype=float32> d_max
tracking <tf.Variable 'batch_renormalization_19/batch_renormalization_19_t:0' shape=(1,) dtype=float32> t
Traceback (most recent call last):
File "mainimprovementcolor.py", line 158, in <module>
model = load_model(args.pretrain, compile=False)
File "C:\Users\127051\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\saving.py", line 492, in load_wrapper
return load_function(*args, **kwargs)
File "C:\Users\127051\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\saving.py", line 584, in load_model
model = _deserialize_model(h5dict, custom_objects, compile)
File "C:\Users\127051\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\saving.py", line 334, in _deserialize_model
' elements.')
ValueError: Layer #1 (named "batch_renormalization_1" in the current model) was found to correspond to layer b'batch_renormalization_1' in the save file. However the new layer batch_renormalization_1 expects 7 weights, but the saved weights have 4 elements.
The real noisy images epoch?
田教授，请问你们的真实噪声训练的批次epochs为多少呢？
Tensorflow version
Hi as you run your code on python 2.7 but in python 3.7 there is no mentioned version 1.3.0 so can you please specify which tensor flow versions are compatible as per your code requirement to run this code as i am facing issue in this
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