# Tag Info

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### What is the math behind median filter's noise reduction property?

Intuition: The intuition is this: Your noise is some event or events that are rare, and that when compared to other events, look like outliers that shouldn't really be there. For example, if you are ...
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### Which Noise Reduction Algorithms Are Used in Commercial RAW Image Processors?

Common Approaches for Commercial Denoisers Commercial denoisers are different than what you'd see on most papers. While on papers the results are mostly using objective metrics (PSNR / SSIM) and are ...
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### Noise reduction using multiple recordings of the same signal

Is it possible to reconstruct the original pure signal? No, that is information-theoretical impossible. Also, that signal doesn't exist, probably, to begin with ;) However, you can definitely ...
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### How to remove the noise without destroying the main edge?

Use bilateral filter or anisotropic diffusion first. The effect of anisotropic diffusion is as the following: . The MATLAB code can be found here. Here is its effect on your image: Finally, non-...
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### What is the math behind median filter's noise reduction property?

Assuming independent random variables with normal distributions, the probability that a value will fall beyond, say, 2 standard deviations will be about 0.01. If you have a median filter of width 3, ...
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### Duration of unknown rectangular pulse with additive white Gaussian noise

You want a method that removes noise while preserving edges. This cannot be achieved well by linear filtering, as you noticed yourself. I know of two approaches that might work well for your problem. ...

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### Removing Gaussian Noise from a Signal to Get Minimum Value

If you have no prior data on the signal of interest there is nothing to do actually. The more prior you have the better you can do. For instance, if the only information you have is the Bandwidth of ...
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### Solve Efficiently the 1D Total Variation Regularized Least Squares Problem (Denoising / Deblurring)

I will answer Total Variation Regularization:  \arg \min_{\boldsymbol{x}} f \left( \boldsymbol{x} \right) = \arg \min_{\boldsymbol{x}} \frac{1}{2} {\left\| A \boldsymbol{x} - \boldsymbol{y} \right\|}...
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### Solving a Weighted Basis Pursuit Denoising Problem (BPDN) with MATLAB / CVX

A MATLAB code which implements the problem as defined and solve it using CVX is given by: ...
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### Can the deconvolution Wiener filter reduce noise without having a blurred image?

For Salt and Pepper noise on medical or real world images using the Wiener Filter isn't recommended. The Wiener filter basically takes advantage only on the knowledge from the spectrum of the data. ...
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### How to Estimated the Noise Standard Deviation (STD - $\sigma$) as a Function of Brightness from an Image?

I didn't read the article you referred to but I can try giving you some idea. Run along the image for each pixel consider its 9 x 9 neighborhood. For each pixel's neighbor hood calculate the STD and ...

### Poisson Noise Removal from an Image

In image denoising far more important then the noise distribution is the noise spatial correlation properties and the prior about the image. Let's try building some cases and dealing with them. The ...

### Image Noise Detection Using MATLAB

I would implement it differently altogether. Since applying the Wiener filter is pretty "cheap" I would create an Image called mWienerFilter. Then: ...
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### Weighted Nuclear Norm Minimization for Image Denoising

Most of the Denoisers in Image Processing make a simple assumption - The data has small number of freedom degrees while noise has high number. Hence if we try to represent the given noisy data with ...

### Separate Signal Values from Noise

I would use the SVD (Singular Value Decomposition). By looking at the Singular Values I'd determine which vectors spread the data and which spread the noise. You may use approach like the Elbow method....

### Using MATLAB xcorr() to Find a Signal Hidden Behind Noise

You're basically doing a bank of hypothesis to find your signal using Matched Filter. Though you use a slightly different method. First of all, you should leave the signal in the time domain and ...
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### Poisson noise and curve fitting - denoise first?

If the example images you've given are at all representative of your application, you may want to consider thinking about the problem a little differently. Instead of thinking of the image as "...