denoising | convolutional image denoiser incorporating camera ISO
kandi X-RAY | denoising Summary
kandi X-RAY | denoising Summary
Fully-convolutional image denoiser incorporating camera ISO values as conditional information.
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QUESTION
I'm trying to separate vocals from a song using a deep learning model. The output is not wrong, but some extra noises cause the signal to sound bad.
The following is 3 seconds of the output file where the noise exists (the areas with a rectangle are the noises):
How can I remove these noises from my output file? I can see that these parts have a different amplitude than the other parts of the songs I want. is there a way to filter the signal based on these amplitudes and only allow a specific amplitude range to exist in my signal?
thanks
UPDATE: Please look at the accepted answer and my code for the denoising algorithm that is working as expected!
...ANSWER
Answered 2022-Mar-02 at 10:09'How can I remove these noises from my output file?
You could 'window' it out (multiply those parts of the signal with a step function at e.g. 0.001 for the noise, and at 1 for the signal). This would silence the noisy regions, and keep your regions of interest. It is however not generalisable - and will work only for a pre-specified audio segment, since the window will be fixed.
I can see that these parts have a different amplitude than the other parts of the songs I want. is there a way to filter the signal based on these amplitudes and only allow a specific amplitude range to exist in my signal
Here you could use two approaches 1) running-window to calculate energy (sum of X^{2} over N samples, where X is your audio signal) or 2) generate the Hilbert envelope for your signal, and smooth the envelope with a window of the appropriate length (perhaps 1-100's of milliseconds long). You can set a threshold based on either the energy or Hilbert envelope.
QUESTION
I am trying to compile a Keras Sequential model (in TF2) in the eager execution mode.
Following is my custom layer:
ANSWER
Answered 2022-Mar-01 at 14:05The direct using of this numpy function is impossible - as it's neither implemented in Tensorflow nor in Theano. Moreover, there is no direct correspondence between tensors and arrays. Tensors should be understood as algebraic variables whereas numpy arrays as numbers. Tensor is an abstract thing and applying a numpy function to it is usually impossible.
But you could still try to re-implement your function on your own using keras.backend. Then you'll use the valid tensor operations and no problem would be raised.
Another way to tackle your problem would be to use tf.numpy_function, see the documentation, this allows you to use numpy functions but there are some limitations.
QUESTION
I am new to Keras, and I am trying to use autoencoder in Keras for denoising purposes, but I do not know why my model loss increases rapidly! I applied autoencoder on this data set:
https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+Classification#
So, we have 756 instances with 753 features. (eg. x.shape=(756,753))
This is what I have done so far:
...ANSWER
Answered 2022-Jan-02 at 16:10The main problem is not related to the parameters that you have used or the model structure but merely coming from the data you use. In the basic tutorials, the authors like to use perfectly pre-processed data to avoid unnecessary steps. In your case, you have possibly avoid the id and class columns leaving you 753 features. On the other hand, I presume that you have standardized your data without any further exploratory analysis and forward to the autoencoder. The quick fix to solve your negative loss which should not make sense with binary crossentropy is to normalize the data.
I used following code to normalize your data;
QUESTION
Here's the code:
...ANSWER
Answered 2021-Nov-12 at 14:09You should use the session state to save this type of information - https://docs.streamlit.io/library/api-reference/session-state
You can think of it as a dictionary that is not lost on page reload.
For your case writing something like
QUESTION
import tensorflow as tf
import keras
def get_model():
x1 = keras.layers.Dense(6, activation='relu',input_shape=(10,))
x2 = keras.layers.Dense(3, activation='relu')(x1)
output_ = keras.layers.Dense(10,acitvation='sigmoid')(x2)
model = keras.model(inputs=[x1], outputs=[output_])
return model
model = get_model()
model.compile(...)
chk_point = keras.callbacks.ModelCheckpoint(f'./best_model.h5',
monitor='val_loss', save_best_only=True, mode='min')
model.fit(..., callbacks=[chk_point])
def new_model():
old = '../best_model.h5' #using old model for training new model
ANSWER
Answered 2021-Nov-07 at 13:53One way to do this is to define the new model, then copy the layer weights from the old model (except for the last layer) and set trainable to False. For example, let's say you want to remove the last layer and add two dense layers (this is just an example). Note that the input and output size of your current model is (10,). Also note that the first layer in the functional API is an input layer. Here is the code:
QUESTION
import torch
import torch.nn as nn
import torch.nn.functional as F
class double_conv(nn.Module):
'''(conv => BN => ReLU) * 2'''
def __init__(self, in_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
class inconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(inconv, self).__init__()
self.conv = double_conv(in_ch, out_ch)
def forward(self, x):
x = self.conv(x)
return x
class down(nn.Module):
def __init__(self, in_ch, out_ch):
super(down, self).__init__()
self.mpconv = nn.Sequential(
nn.MaxPool2d(2),
double_conv(in_ch, out_ch)
)
def forward(self, x):
x = self.mpconv(x)
return x
class up(nn.Module):
def __init__(self, in_ch, out_ch, bilinear=True):
super(up, self).__init__()
# would be a nice idea if the upsampling could be learned too,
# but my machine do not have enough memory to handle all those weights
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
else:
self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)
self.conv = double_conv(in_ch, out_ch)
def forward(self, x1, x2):
x1 = self.up(x1)
diffX = x1.size()[2] - x2.size()[2]
diffY = x1.size()[3] - x2.size()[3]
x2 = F.pad(x2, (diffX // 2, int(diffX / 2),
diffY // 2, int(diffY / 2)))
x = torch.cat([x2, x1], dim=1)
x = self.conv(x)
return x
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
class UNet(nn.Module):
def __init__(self, n_channels, n_classes):
super(UNet, self).__init__()
self.inc = inconv(n_channels, 64)
self.down1 = down(64, 128)
self.down2 = down(128, 256)
self.down3 = down(256, 512)
self.down4 = down(512, 512)
self.up1 = up(1024, 256)
self.up2 = up(512, 128)
self.up3 = up(256, 64)
self.up4 = up(128, 64)
self.outc = outconv(64, n_classes)
def forward(self, x):
self.x1 = self.inc(x)
self.x2 = self.down1(self.x1)
self.x3 = self.down2(self.x2)
self.x4 = self.down3(self.x3)
self.x5 = self.down4(self.x4)
self.x6 = self.up1(self.x5, self.x4)
self.x7 = self.up2(self.x6, self.x3)
self.x8 = self.up3(self.x7, self.x2)
self.x9 = self.up4(self.x8, self.x1)
self.y = self.outc(self.x9)
return self.y
ANSWER
Answered 2021-Jun-11 at 09:42Does n_classes signify multiclass segmentation?
Yes, if you specify n_classes=4 it will output a (batch, 4, width, height) shaped tensor, where each pixel can be segmented as one of 4 classes. Also one should use torch.nn.CrossEntropyLoss for training.
If so, what is the output of binary UNet segmentation?
If you want to use binary segmentation you'd specify n_classes=1 (either 0 for black or 1 for white) and use torch.nn.BCEWithLogitsLoss
I am trying to use this code for image denoising and I couldn't figure out what will should the n_classes parameter be
It should be equal to n_channels, usually 3 for RGB or 1 for grayscale. If you want to teach this model to denoise an image you should:
- Add some noise to the image (e.g. using
torchvision.transforms) - Use
sigmoidactivation at the end as the pixels will have value between0and1(unless normalized) - Use
torch.nn.MSELossfor training
Because [0,255] pixel range is represented as [0, 1] pixel value (without normalization at least). sigmoid does exactly that - squashes value into [0, 1] range, hence linear outputs (logits) can have a range from -inf to +inf.
Why not a linear output and a clamp?
In order for the Linear layer to be in [0, 1] range after clamp possible output values from Linear would have to be greater than 0 (logits range to fit the target: [0, +inf])
Why not a linear output without a clamp?
Logits outputted would have to be within [0, 1] range
Why not some other method?
You could do that, but the idea of sigmoid is:
- help neural network (any logit value can be outputted)
- first derivative of
sigmoidis gaussian standard normal, hence it models the probability of many real-life occurring phenomena (see also here for more)
QUESTION
Trying to run---
from keras.optimizers import SGD, Adam,
I get this error---
Traceback (most recent call last):
File "C:\Users\usn\Downloads\CNN-Image-Denoising-master ------after the stopping\CNN-Image-Denoising-master\CNN_Image_Denoising.py", line 15, in
from keras.optimizers import SGD, Adam
ImportError: cannot import name 'SGD' from 'keras.optimizers'
as well as this error, if I remove the SGD from import statement---
ImportError: cannot import name 'Adam' from 'keras.optimizers'
I can't find a single solution for this.
I have Keras and TensorFlow installed. I tried running the program in a virtualenv (no idea how that would help, but a guide similar to what I want mentioned it) but it still doesn't work. If anything, virtualenv makes it worse because it doesn't recognize any of the installed modules. I am using Python 3.9. Running the program in cmd because all the IDEs just create more trouble.
I am stumped. My knowledge of Python is extremely basic; I just found this thing on GitHub. Any help would be greatly appreciated.
...ANSWER
Answered 2021-May-19 at 14:34Have a look at https://github.com/tensorflow/tensorflow/issues/23728:
from tensorflow.keras.optimizers import RMSprop
instead of :
from keras.optimizers import RMSprop
It worked for me.
QUESTION
This is my example image:
You can see in the bottom left corner and on the edge of the main structure, there is a lot of noise and outlier green pixels. I'm looking for a way to remove them. Currently, I have tried the following:
...ANSWER
Answered 2021-May-12 at 08:32Try this:
QUESTION
I've written a denoising function with cv2 and concurrent.futures, to be applied on both my training and test image data.
The functions (at current) are as follows:
...ANSWER
Answered 2021-May-04 at 19:46You can change the iterable in executor.map to be a tuple of arguments, which can then split in your other function.
QUESTION
As follows, my task is to use AR modeling to remove artifacts from noisy signals. Let's say I have ECG or EMG in raw data. On IEEE I have found that this is possible via Wavelet transform, Butterworth filters or Empirical mode decomposition.
https://www.kaggle.com/residentmario/denoising-algorithms#Machine-learning-models
Raw EMG:
What exactly am I supposted to do with Auto Regression model? As I understand it right now it is used to forecast the data.
...ANSWER
Answered 2021-Mar-25 at 17:04As I understand it right now it is used to forecast the data.
Yes, that's a common case for AR(p) models; but in order to forecast, its parameters should be estimated and it is done over the observations you provide to it. Therefore you can have so-called "fitted values" and use them as the "denoised" version of the signal at hand. This is because AR(p) is this:
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Install denoising
You can use denoising like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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