convolution

问题 I am currently trying to compare a simple inverse filter to the wiener filter for deconvolution using matlab. My starting signal is exp(-t^2) and this is to be convolved with a rect that is nonzero for times -.5 to .5. I am introducing noise with amplitude in the range -.5 to .5. Defining my time domain to frequency domain mapping: f = exp(-t^2) => F s = rect => R c = f*s => C r = noise (see above) => R with noise c becomes: c = f*s + n => C = FxS + N For the first approach I am simply taking

问题 I am trying to augment a network. For the convolution part, I am using convlstm2d from keras. Is there a process to perform deconvolution ( i.e. lstmdeconv2d ? ) 回答1: There is Conv3D for that, checkout this example used to predict the next frame 回答2: It should be possible to combine any model with the TimeDistributed wrapper. So you can create a deconv-model, and apply it on the output (which is a sequence of vectors) of the LSTM using the TimeDistributed wrapper. An example. First create a

问题 I am trying to augment a network. For the convolution part, I am using convlstm2d from keras. Is there a process to perform deconvolution ( i.e. lstmdeconv2d ? ) 回答1: There is Conv3D for that, checkout this example used to predict the next frame 回答2: It should be possible to combine any model with the TimeDistributed wrapper. So you can create a deconv-model, and apply it on the output (which is a sequence of vectors) of the LSTM using the TimeDistributed wrapper. An example. First create a

问题 I want to compute this integral $\frac{1}{L}\int_{-\infty}^{t}H(t^{'})\exp(-\frac{R}{L}(t-t^{'}))dt^{'}$ using numpy.convolution, where $H(t)$ is heavside function. I am supposed to get this equals to $\exp(-\frac{R}{L}t)H(t)$ below is what I did, I changed the limitation of the integral into -inf to +inf by change of variable multiplying a different H(t) then I used this as my function to convolve with H(t)(the one inside the integral), but the output plot is definitely not a exp function,

问题 Suppose I have an (m x n) 2-d numpy array that are just 0's and 1's. I want to "smooth" the array by running, for example, a 3x3 kernel over the array and taking the majority value within that kernel. For values at the edges, I would just ignore the "missing" values. For example, let's say the array looked like import numpy as np x = np.array([[1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 1, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 0, 1,

问题 Suppose I have an (m x n) 2-d numpy array that are just 0's and 1's. I want to "smooth" the array by running, for example, a 3x3 kernel over the array and taking the majority value within that kernel. For values at the edges, I would just ignore the "missing" values. For example, let's say the array looked like import numpy as np x = np.array([[1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 1, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 0, 1,

问题 Suppose I have an (m x n) 2-d numpy array that are just 0's and 1's. I want to "smooth" the array by running, for example, a 3x3 kernel over the array and taking the majority value within that kernel. For values at the edges, I would just ignore the "missing" values. For example, let's say the array looked like import numpy as np x = np.array([[1, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 1, 0], [0, 0, 1, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 0, 1,

问题 I'm trying to use scipy 's ndimage.convolve function to perform a convolution on a 3 dimensional image (RGB, width, height). Taking a look here: It is clear to see that for any input, each kernel/filter should only ever have an output of NxN , with strictly a depth of 1. This is a problem with scipy , as when you do ndimage.convolve with an input of size (3, 5, 5) and a filter/kernel of size (3, 3, 3) , the result of this operation produces an output size of (3, 5, 5) , clearly not summing

问题 The Fourier transform of the convolution (with stride 1) of two images is equivalent to point-wise multiplication of their individual Fourier transforms. I need to perform stride-'n' convolution using the above FFT-based convolution. For some reasons I need to operate in the frequency domain itself after taking the point-wise product of the transforms, and not come back to space domain by taking inverse Fourier transform, so I cannot drop the excess values from the inverse Fourier transform

问题 The Fourier transform of the convolution (with stride 1) of two images is equivalent to point-wise multiplication of their individual Fourier transforms. I need to perform stride-'n' convolution using the above FFT-based convolution. For some reasons I need to operate in the frequency domain itself after taking the point-wise product of the transforms, and not come back to space domain by taking inverse Fourier transform, so I cannot drop the excess values from the inverse Fourier transform