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7

There are, and I am looking forward to seeing the specifics of other answers, but one way to deal with this is to not have the noise (or as much noise) in the source data to begin with. The noise is coming from the fact that there is high variance in the rendering - the number of samples you've taken haven't converged enough to the actual right answer of ...


5

Is denoising ALWAYS about doing a low pass filter / blur? No, but this is the most obvious technique. A good denoiser isn't just a filter that runs on the image, but actually performs the reconstruction; i.e. it's a function from random samples to an image, not a function from an image to an image. Or are there other ideas and techniques for removing ...


4

One technique you could use is break the image into blocks and measure each blocks variance - this way you can apply more samples to blocks with higher variance. The variance can be estimated by using 2 accumulation buffers instead of 1. You render each pass into an alternate buffer. The absolute difference between these buffers (with respect to each block)...


3

Elaboration on temporal solve: I don't have much concrete info for you, but I'm drawing from the idea of "temporal anti aliasing". Basically, if a camera was stationary, you could average pixel values over the last N frames, possibly using harmonic mean or something else like that to help filter out spikes. The result would be a cleaner, less noisy, more ...


2

Besides the suggestions in comments, I would suggest a high pass filtering to get rid of the low frequency noise, before running your local thresholding. Here an example with GIMP, that you can probably easily reproduce with opencv as well.


2

It certainly isn't always about low pass filters (see for example here on WP on "Noise Reduction") but you have to keep in mind that in your case the noise will always have a high frequency because you can basically consider each pixel with a independent noise realization. So any way of removing noise in this situation will have a low pass effect.


2

A fairly basic but effective technique is median filtering. For video, you can apply it (spatio)temporally by replacing the value of each pixel in each frame by the median of the values of the pixel (and its neighbors) in the current and the N previous and later frames. A nice feature of median filtering is that it preserves linear edges (and, when used ...


1

In 2009 it seems there was a development of an hierarchical and comparison motion detection algorithm that was proposed in this paper: Motion detection: fast and robust algorithms for embedded systems They managed to get a decent reduction of noise, as you can see in the last image of the paper. It seems it's the "morphological post-processing" that ...


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