4
votes
Accepted
Estimating Convolution Kernel from Input and Output Images
While it can be done in Frequency Domain it requires delicate handling of the edges (Discrete Frequency Domain assumes Periodic Signals).
Hence I think the best approach is to build this problem as an ...
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3
votes
Deconvolution with unknown impulse response
What methods are there for such deconvolution problems,
None for the general case (without additional information). We can easily see this by looking at the frequency domain
$$y(t) = h(t)*x(t) \...
- 48.2k
3
votes
Accepted
Blind source separation for asynchronously observed mixture channels
Based on a few quick experiments, ICA fails when the channels are delayed relative to each other. It's fairly easy to test in MATLAB (or no doubt other software packages) with FastICA (or Robust ICA, ...
- 2,064
3
votes
Accepted
Doubt: Signal detection in noise Part 1
You use the matched filtering technique when you search measurement data for a signal of a given form. You know the signal waveform in advance and either you are not sure if the signal is present in ...
- 1,774
2
votes
How is Point Spread Function (PSF) related to Image Priors in Blind Deconvolution?
A "point spread function" denotes how a single point from the source, or a punctual object (mathematically a Dirac) would spread on the observed image through the imaging system.
In blind ...
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2
votes
Estimating Convolution Kernel from Input and Output Images
Take Fourier Transform of both original image and blurred image, then divide Fourier transform of blurred image by Fourier transform of original image. This will give you the Fourier transform of ...
- 1,417
2
votes
Estimate peak width from a vector that is a superposition of unknown number of identical Gaussian peaks with different heights?
My first comment would be why the heck are you using R if you are concerned with processing speed, or are you just prototyping algorithms?
Anyway, Without getting into how I derived it, here is a ...
- 7,600
2
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Estimate peak width from a vector that is a superposition of unknown number of identical Gaussian peaks with different heights?
Ha just figured out a faster and better method just using BIC-optimized selection of optimal peak width, using a banded covariate matrix with shifted Gaussian peak shapes of given width & using ...
- 151
2
votes
Finding the Best Gaussian Smoothing Kernel to Minimize the Discrepancy Between Two Images
Considering that the convolution is a multiplication in the Fourier domain, this problem can be converted to a very simple fitting problem.
$B$ is a blurred version of $A$. Thus we have $\hat{B} = \...
- 2,770
2
votes
Accepted
Finding the Best Gaussian Smoothing Kernel to Minimize the Discrepancy Between Two Images
This is closely related to Blind Deconvolution.
The only difference is we limit our self to a very specific type of blur kernels.
The nice thing about the (Centered) Gaussian Kernel is being defined ...
- 20.5k
1
vote
Accepted
Error plot between known and estimated data
Your formula/method for computing MSE between estimated and known inputs looks good to me. For symbol error rate you could use something like a Hamming distance which simply counts the number of times ...
- 4,144
1
vote
Accepted
Looking for pratical quantitative comparison metrics for scaled, delayed and warped Signals
I'm answering the question the way I understood it - How can one find a similarity measure which isn't sensitive to scaling and shifting.
An approach could be borrowed from the Computer Vision world ...
- 20.5k
1
vote
Differences Using Maximum Likelihood or Maximum a Posteriori for Deconvolution / Deblur
You can think of MAP as a regularization of the ML.
Just like you have regularization for Least Squares Problem (They can be built, mostly, as MAP problem).
The nice thing is that, as always, the ...
- 20.5k
1
vote
Accepted
How to Select Point Spread Function Empirically for Image Deconvolution?
One way, though not directly visual, is to observe the image statistics.
We have a pretty good idea about the statistics of Natural Images, more specifically, their Gradient Distribution (See ...
- 20.5k
1
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Deconvolution Question on Article "Deriving Intrinsic Images from Image Sequences" by Yair Weiss
I'm struggling a little with this notation because I don't usually do any significant image processing. Many LTI filters have an inverse and it seems like this is where your discussion is leading.
I'm ...
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