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Hi Haitham, 

One very (VERY) rudimentary approach that I have used for this problem is apply 3D Gaussian blur to the image series in Fiji. Generally I start with small settings like 0.7/0.7/0.7 and increase them as needed; higher blur settings will smooth noise more aggressively but moving objects will become much more blurred.  Gaussian 3D filtering will significantly reduce noise while persistent and slow-moving objects will stand out much better from the background.  The trick is you need either a slow-moving target or very fast acquisition.  If your target moves its own diameter or more per frame then it will get filtered out as noise.  

I should caution that we mostly used this trick to visually identify rhythmically moving structures (beating cilia) rather than to do proper PIV analysis. For quantitative experiments your mileage may vary.  

Best, 


Tim

Timothy Feinstein, Ph.D. 
Research Scientist
University of Pittsburgh Department of Developmental Biology


On 5/30/17, 12:35 PM, "Confocal Microscopy List on behalf of Haitham Shaban" <[log in to unmask] on behalf of [log in to unmask]> wrote:

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    Dear list members,
    
    I am using particle image velocimetry (PIV) methods to estimate velocity fields between image pair from confocal microscopy, which is usually subject to a mixture of noise (basically Gaussian and Poisson). As the motion estimation is very sensible to noise, are there any denoising methods which can be used to distinguish between noise and real movement?, especially when the motion is expected to be small.
    
    I already tried with fixed samples and the noise signal is still giving velocity fields (close to the real motion). I thought in a direction like the following: calculate the temporal derivative of image 1 and 2 (in the simplest case, subtract the images), if the resulting image contains only noise, don't proceed it. However, this does not denoise images.
     Also, I don't know how to distinguish if the resulting image is noise or not?. 
    
    Thank you
    Haitham