How To Remove People From Photos in Photoshop – Image Stack Tutorial

In this tutorial, you’re going to learn how to remove people from photos in Photoshop!

This is a follow up to last weeks tutorial, How to Remove Anything from a Photo in Photoshop. In that tutorial, we used manual tools like the Spot Healing Brush Tool, Patch Tool, and Clone Stamp Tool, to remove the distracting element. However, in this video, we will see how you can let the camera do all the hard work.

For this Photoshop tutorial, we’re going to use Image Stacks to remove people walking through your photos. This is a fantastic technique that requires a bit of planning and the use of multiple photos, but the results are astonishing!

The Image Stack Mode method is perfect for removing people from crowded places or unwanted objects that are moving through a scene.

We will use the Mean Stack Mode which will take a statistical average of the content found in all the photos that we will use for this tutorial. This means that it will keep identical areas and remove everything that changes between the different photos.

It is very likely that cars and people will move and change locations and will be removed when the Stack Mode is applied, leaving only the background.

When you’re out taking photos make sure that your camera is on a tripod so that the images line up during the blend. If you do not have a tripod, make sure that stand very still and hold your camera as steady as possible.

Wait about 25 seconds or so between each photo that you take so that you give people enough time to move. In most cases, you will only need between 10 to 20 photos, but take more just in case.

In this tutorial, we’re going to use fourteen photos that I shot with my cell phone without using a tripod. I wanted to use photos that were not shot under the perfect conditions so that you could see the power of this technique.

Stack Modes

Stack modes operate on a per-channel basis only, and only on non-transparent pixels.

For example, the Maximum mode returns the maximum red, green, and blue channel values for a pixel cross-section and merges them into one composite pixel value in the rendered image.

Mode	Result	Comments
Entropy	entropy = – sum( (probability of value) * log2( probability of value) ) Probability of value = (number of occurrences of value) / (total number of non-transparent pixels)	The binary entropy (or zero order entropy) defines a lower bound on how many bits would be necessary to losslessly encode the information in a set.
Kurtosis	kurtosis = ( sum( (value – mean)4 ) over non-transparent pixels ) / ( ( number of non-transparent pixels – 1 ) * (standard deviation)4 ).	A measure of peakedness or flatness compared to a normal distribution. The kurtosis for a standard normal distribution is 3.0. Kurtosis greater than 3 indicates a peaked distribution, and kurtosis less than 3 indicates a flat distribution (compared to a normal distribution).
Maximum	The maximum channel values for all non-transparent pixels
Mean	The mean channel values for all non-transparent pixels	Effective for noise reduction
Median	The median channel values for all non-transparent pixels	Effective for noise reduction and removal of unwanted content from the image
Minimum	The minimum channel values for all non-transparent pixels
Range	Maximum minus the minimum of the non-transparent pixel values
Skewness	skewness = (sum( (value – mean)3) over non-transparent pixels ) / ( ( number of non-transparent pixels – 1 ) * (standard deviation)3 )	Skewness is a measure of symmetry or asymmetry around the statistical mean
Standard Deviation	standard deviation = Square Root(variance)
Summation	The sum channel values for all non-transparent pixels
Variance	variance = (sum( (value-mean)2 ) over non-transparent pixels ) / ( number of non-transparent pixels – 1)