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10 votes
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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 ...
Royi's user avatar
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9 votes

Why does signal averaging reduces noise levels by more than $\sqrt{n}$?

Well, I would say the assumption that your noise is Gaussian is ill fitting. If the noise is due to machine interference, it probably has some tonal characteristics. Tones of the same frequency can ...
Cedron Dawg's user avatar
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9 votes
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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 ...
Marcus Müller's user avatar
7 votes

Estimators for improved spectral subtraction of noise

Maximum likelihood (ML) estimator Here will be derived a maximum-likelihood estimator of the power of the clean signal, but it doesn't seem to be improving things in terms of root mean square error, ...
Olli Niemitalo's user avatar
6 votes
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The Meaning of the Terms Isotropic and Anisotropic in the Total Variation Framework

In the Total Variation framework we define 2 flavors: $$ \text{Isotropic TV} \; {TV}_{ {L}_{2} } \left( X \right) = \sum_{ij} \sqrt{ { \left( {D}_{h} X \right) }_{ij}^{2} + { \left( {D}_{v} X \right) ...
Royi's user avatar
  • 19.8k
5 votes

Removing Noise from Dental Radiography

As far as I understood, by image derivation you mean extracting edges. I would recommend to filter the image by a relatively large Gaussian filter. If computational cost of image derivation is ...
MimSaad's user avatar
  • 1,976
5 votes

Estimators for improved spectral subtraction of noise

Update: I'm sorry to have to say that testing shows the following argument seems to break down under heavy noise. This is not what I expected, so I have definitely learned something new. My prior ...
Cedron Dawg's user avatar
  • 7,590
5 votes

Bibliographic References on Denoising Distributed Acoustic data with Deep Learning

I'll compare this to the problem of equalization in optical fibres. Assume a single straight fibre for a start, and neglect noise. There's some localized phenomenon that effects a pressure wave to ...
Marcus Müller's user avatar
4 votes
Accepted

Inverse Fast Fourier with overlap

There are several ways. Start with taking the iFFT of $ S^{(1)} $ and $ S^{(2)} $. Let's call the results $ m^{(1)} $ and $ m^{(2)} $, since they are modified. They are back in the time domain and ...
Cedron Dawg's user avatar
  • 7,590
4 votes
Accepted

A Simple Algorithm to Filter / Smooth / Denoise a Noisy Staircase Graph

Assuming you meant to produce something similar to the green line: What about $$\text{output}[n] = \max\{\text{input}[n-k], \text{input}[n-k+1], \ldots ,\text{input}[n]\}$$ i.e. you just find the ...
Marcus Müller's user avatar
4 votes

Help with denoising signal and periodogram analysis resources

Firstly, I am confused if I am supposed to filter my signals to get rid of any frequencies above the Nyquist frequency. My sampling frequency is 32Hz and my time series is somewhat noisy and has some ...
Marcus Müller's user avatar
4 votes

What is the advantage of Wiener filter for noise reduction of a 1D signal?

The Wiener filter considers statistical behaviours of the noise and the signal, and thus, (theoretically) achieves optimum separation of them for a class of signals and systems, which is not the case ...
Fat32's user avatar
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4 votes
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What process could generate this kind of noise in the multitaper spectrum?

I suspect that the noise may be a frequency-drifting periodic signal with a fundamental frequency of around 660 Hz. A periodic function $x$ satisfies $x(t + P) = x(t)$ for a fundamental period $P > ...
Olli Niemitalo's user avatar
3 votes
Accepted

Additive White Gaussian Noise (AWGN) and Undecimated DWT

One of the benefits of DWT is that it is an orthonormal transform Well, not quite. Some standard DWT are orthonormal, but not all of them. The others used in practice are biorthogonal. Which makes ...
Laurent Duval's user avatar
3 votes

Wavelet image denoising: dual-tree versus double-density

The formalism of the continuous wavelet transform is relatively flexible. To make a practical tool out of it, you ought to discretize it, and here come the pain. It is quite easy to discretize it in a ...
Laurent Duval's user avatar
3 votes
Accepted

Gradient of Total Variation (TV) Norm in Total Variation Denoising

I am by no means an expert on total variation, however I think you should check out this Wikipedia page. It doesn't directly answer your question, but I believe the lemma below illustrates the ...
Brady Sheehan's user avatar
3 votes

What is the difference between MSE and PSNR of images?

What is the difference between MSE and PSNR of images? Very little. Almost nothing, in a relative way. Most error measures are relative to certain quantities or references. If you have $K$ samples $...
Laurent Duval's user avatar
3 votes

Using Total Variation Denoising to Clean Accelerometer Data

If your data model is Piece Wise Smooth Signal then you should use Total Variation as regularization. Let's try comparing 2 methods for Denoising with 2 different regularization (Both works on the ...
Royi's user avatar
  • 19.8k
3 votes

When and how does one use a Wiener filter?

Perhaps a motivating radar/audio example would be an adaptive sidelobe canceller and adaptive noise cancellers. Rather than just show equations, let's walk through some text descriptions: Let's say ...
vintagevogue's user avatar
3 votes

Remove a known wav file from recorded file

Assuming that there are no non-linear effects between the sound N source and the microphone (such as AGC), you might try to estimate the impulse response of the channel between the source for sound N ...
hotpaw2's user avatar
  • 35.4k
3 votes

Denoise Techniques When Clean Signal and Pure Noise Are Available

I'm not sure I understand the question, but if you have the exact waveform you want to recover, you can basically employ a matched filter to detect the existence of the signal in the acquired data. ...
LDPC's user avatar
  • 195
3 votes

What is the best input for de-noising autoencoder for sound data?

As you mentioned MFCC features are one of the best features to represent audio as it captures both the time and frequency variations in the audio clip.You can get more details about MFCCS features in ...
Navaratnarajah Suman's user avatar
3 votes

Machine learning for denoising MRI images

My personal feeling is that you should do each things separately and compare the results. For example, take your MRI dataset and denoise using "standard algorithm 1", "standard algorithm 2" and "...
brechmos's user avatar
  • 131
3 votes
Accepted

Complex output after inverse FFT of a real signal

Essentially, your code does not respect the inherent Hermitian symmetry of the output of the FFT. Here, your signal is odd-sized $2K+1$. Hence, this FFT yields a complex vector of coefficients $d$ (...
Laurent Duval's user avatar
3 votes

Noise reduction using multiple recordings of the same signal

As Stanley Pawlukiewicz said: even under ideal circumstance, you can gain 3 dB of SNR per doubling of recordings. I.e., to increase SNR by, say, 15 dB, you'd need to average $$ 2^{\frac{15}{3}} = 2^{5}...
leftaroundabout's user avatar
3 votes

Salt and Pepper impulse Denoising opencv

Can we consider these noises as salt and pepper noise.? Is there something else that I am missing? several pixels getting erased to either zero or one -> yeah, that fits my definition of salt and ...
Marcus Müller's user avatar
3 votes

Can discrete wavelet transform for denoising purposes be implemented in real time?

Even though I'm not much of a wavelet expert, I can answer with a definitive YES!! Now, do you have any messy details to complain about, like the size of the box, the power that it consumes, or the ...
TimWescott's user avatar
  • 12.9k
3 votes

Help with denoising signal and periodogram analysis resources

The Fourier transform of a sampled (discrete time) signal can only have information between -Fs/2 and +Fs/2, and that information repeats such that X(f +Fs) = X(f), such that Fs is the sampling ...
Dan Szabo's user avatar
  • 1,038

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