housespasob.blogg.se - Why denoiser iii removes masks in after effects

Method specifies the most complex operation the Warp Stabilizer VFX performs on the footage to stabilize it: Position, which is based on position data only Position, Scale, Rotation, which uses these three types of data Perspective, which effectively corner-pins the entire frame or Subspace Warp (the default), which attempts to warp various parts of the frame differently to stabilize the entire frame.

No Motion attempts to remove all of the camera motion. Smooth Motion makes camera movement smoother, but doesn’t eliminate it use the Smoothness setting to control how smooth the movement becomes. Denoising should be done at linear stage prior to this.This is just a summary of the Warp Stabilizer VFX settings to help you get started. To reiterate - both articles say the same thing - denoise while data is still linear - this has nothing to do with multichannel combining, or making color (false or otherwise) image out of individual mono masters. If you do it "prior to combining" (meaning prior to stacking) - you will end up skewing statistics of signal and noise and stacking (or here called "combining") will not produce wanted results - down the thread on PI forum OP says that he tried that with result of loss of faint detail - this is why you should not denoise individual subs prior to stacking (or "combining"), but rather after stacking / "combining", but prior phase 3 while data is still linear. What has confused you is that it uses term combine instead of stacking and that warning about doing it after "combine" (stacking) is referring to the fact that you should do it after phase 2 and not after phase 1 - after calibration. First article about MURE denoise from PI forums says that you should do it after phase 2 but prior to phase 3.

What is important is that both articles place denoise phase at the same place in the workflow. In some workflows 3 and 4 can swap places. Let's quickly "run thru" stages of data reduction and processing: Here it advises you to do denoise at linear stage.

My personal noise reduction routine of choice for the images we are about to post-process was using MultiscaleLinearTransform (as described in the tutorial on noise reduction) with stretched clone copies of the images themselves acting as masks. Please note that this is beyond the scope of this tutorial and is covered amply by another tutorial specially written on the subject of noise reduction. The choice of whether or not to do this, or how aggressively to do it, depends on the level of noise in your images. "Though your images may differ, it is common to apply some noise reduction to images in their linear state. Here is quote from LV article that you just linked (I just did quick search on term noise and this is pretty much where it appears in text): I think you are confused with ambiguous usage of term "combine". The LV tutorial ( ) advises the opposite - perform denoising before combining. Combined images must be equally exposed, have the same pixel resolution, and be registered by projective transformation with no distortion correction." Signal-to-noise ratio (SNR) will be enhanced by combining noisy images and denoising the result.

The PI forum article (the link I've sent) says in Warning (Note) 2 : " Do not combine denoised images. Non linear transform skews relationship between signal strength and associated noise in Poisson distribution. It also says that during registering and combining you need to be careful not to do any sort of projective corrections (like fixing lens distortion in wide field images and such).īecause this algorithm utilizes noise distribution statistics - it needs linear data, so you should apply it after stacking and before any sort of non linear transform (histogram stretch). Refers to exactly what I mentioned - stacking of denoised images vs denoising stack and not channel combine. It assumes image is combination of Poisson and Gaussian noise distributions and does some clever math to remove associated noise. From what I can see in first post of that topic you linked to, MURE denoise works as first type of algorithm that I described.