# Questions tagged [deconvolution]

in mathematics, the inverse operation of convolution signals. In general, the purpose of deconvolution is to find solutions of the convolution equation defined as: f * g = x. Where h is the recorded signal, and f is a signal that you want to recover, and we know that the first signal is obtained by convolution of the second with some known signal g. If the signal g is unknown, it has to be estimated (eg. statistical estimation).

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### Deconvolution of 1D Signals Blurred by a Gaussian Kernel

I have convolved a random signal with a a Gaussian and added noise (Poisson noise in this case) to generate a noisy signal. Now I would like to deconvolve this noisy signal to extract the original ...
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### How to Deduce a Linear System's Impulse Response from a Set of Input and Output Signals?

I want to know how to solve those types of problems.. is it by inspection ? Consider the linear system below. When the inputs to the system $x_1[n]$, $x_2[n]$ and $x_3[n]$, the responses of the ...
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### Deconvolution - Richardson Lucy vs. Wiener Filter

I am studying some deconvolution techniques, In order to remove motion blur, like: Richardson-Lucy Wiener Are there any pros / cons of using one versus another? For example which are the pros / cons ...
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### Can every type of linear filter be modelled by a convolution?

I have an input time series going through a filter that creates another time series as output. If I assume in first approximation that my filter is linear, does it necessarily mean that I can model ...
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### Inverse Problem / Deconvolution with Pink Noise

Hi I dived somewhat into deconvolution of systems which can be described as: $s(t) = o(t) * h(t) + n(t)$ where $s$ is my measured 1D time resolved signal, $o$ is the original signal $h$ is the ...
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### Recover Filter Coefficients from Filtered Noise

I have a digital signal which may be represented as a pulse noise source filtered with an FIR (finite impulse response) filter. Suppose that the noise consists of discrete pulses (nonzero samples ...
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### Deconvolving a 1d Signal Using a Lookup Table

assuming I measure a signal that has different PSFs per position in time. for example: ...
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### Finding the Width of a Point Spread Function (PSF)

I have a binary image that has been convolved with the following PSF: $$\mathrm{PSF}(x,y)=\cosh^{-2}\left(\frac{\sqrt{x^2+y^2}}{w}\right)$$ where $w$ is unknown. The image binary image generally ...
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### Generate the Matrix Form of 1D Convolution Kernel

As a follow up to Generate the Matrix Form of 2D Convolution Kernel, could someone explain how to generate the matrix form of a 1D convolution kernel? How different convolutions shapes are handled? ...
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### The Gradient / Derivative of Least Squares of 2D Image Convolution

Given the objective function: $$\frac{1}{2} {\left\| h \ast x - y \right\|}_{2}^{2}$$ Where $h$ is the 2D convolution kernel and $x$ is the 2D convolution image and $y$ is a given 2D image. ...
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### Removing Gaussian Noise from a Signal to Get Minimum Value

I have a signal that has a minimum value that I'm trying to read. The issue I'm having is that the signal is spread out by gaussian noise. I have the signal at a lot of timesteps (and expect the ...
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I would like to solve the following Image Deconvolution equation by ADMM. $$\mathbf { \min\frac{1}{2}\Vert{Cx-b}\Vert_2^2+\Vert w\circ (D x)\Vert_1 \tag 1}$$ Where, $x$ is a vector of unknown pixel ...