How to extract the layers from an image (jpg,png,etc)

Question

Given an image such as the CakePHP logo, how can this image be converted back into a PSD with the layers. As a human, I can easily work out how to translate this back to a PSD with layers. I can tell that the background is a circular shape with star edges. So the circular star part is at the back, the cake image is on top of this and the words CakePHP is over all of these two images.

I can use Photoshop/Gimp tools to separate these images into three images and fill in the areas in-between. Then I have three layers.

As a human, it is easy to work out the layering of most logos and images and many images have multiple layers, the CakePHP logo is just one example. Images in the real world also have a layering, there may be a tree layer on top of a background of grass. I need a general way to convert from an image back to the layered representation, ideally a software solution.

In absence of a programmed solution, are there any papers or research which solve this problem or are related to this problem? I am mostly interested in converting human constructed images such as logos or website titles back to layered representation.

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I want to point out some benefits of doing this, if you can get this image to a layered representation automatically then it is more easy to modify the image. For example, maybe you want to make the cake smaller, if the computer already layered the cake on top of the red background, you can just scale the cake layer. This allows for layer adjustment of images on websites which do not have layer information already.

Answer 1

When you convert from a layer representation to an image you are loosing information. For instance, you don't know the values of the pixels of the background layer behind the cake. Additionally, you don't know for sure which part of the image belong to which layer.

However it may be possible in some cases to recover or estimate at least partially this information. For instance, you could try to separate an image into "layers" using segmentation algorithms. On your exemple, a simple segmentation based on color would probably work.

As for recovering lost pixel values in the background, there is so-called inpainting technics which attempt to estimate missing areas in images based on its surroudings.

Lastly, to recover position and content of texts in images you can rely on Optical Character Recognition (OCR) methods.

Keep in mind that there is no simple algorithm to solve your problem which is more complex than it seems. However, using the above information, you can try to automate at least partially your problem.

Answer 2

As already mentioned, this is a non-trivial task. Ultimately, it can be most simply phrased as: given an image (or scene if real photo) which is composed of pixels N, how can those be assigned to M layers?

For segmentation, it's all about the prior knowledge you can bring to bear to this as to what properties of pixels, and of groups of pixels, give "hints"(and I use the word advisedly!) as to the layer they belong to.

Consider even the simplest case of using just the colour in your image. I can generate these 5 "layers" (for hue values 0,24,90, 117 and 118):

With this code (in python/opencv)

import cv 

# get orginal image
orig = cv.LoadImage('cakephp.png')

# show original 
cv.ShowImage("orig", orig)

# convert to hsv and get just hue
hsv = cv.CreateImage(cv.GetSize(orig), 8, 3) 
hue = cv.CreateImage(cv.GetSize(orig), 8, 1) 
sat = cv.CreateImage(cv.GetSize(orig), 8, 1) 
val = cv.CreateImage(cv.GetSize(orig), 8, 1) 
cv.CvtColor(orig, hsv, cv.CV_RGB2HSV)
cv.Split(hsv,hue,sat,val,None)
#cv.ShowImage("hue", hue)

# loop to find how many different hues are present...
query = cv.CreateImage(cv.GetSize(orig), 8, 1) 
result = cv.CreateImage(cv.GetSize(orig), 8, 1) 
for i in range(0,255):
  cv.Set(query,i)
  cv.Cmp(query,hue,result,cv.CV_CMP_EQ)
  # if a number of pixels are equal - show where they are 
  if (cv.CountNonZero(result)>1000): # <-what is signficant?
    cv.ShowImage(str(i),result)
    cv.SaveImage(str(i)+".png",result)
    cv.WaitKey(-1)

But, even here we are having to describe what is "significant" in terms of the number of pixels that belong to a mask (to the extent that we can miss some colours). We could start to cluster similar colours instead - but at what density does a cluster become significant? And if it wasn't just pure colour, but textured instead, how could we describe this? Or, what about inference that one layer is part of another, or in front of it? Or, ultimately, that some of the layers seem to be what we humans call "letters" and so should probably be all related...

A lot of the research in Computer Vision in segmentation generally tries to take this problem and improve it within a framework that can encode and apply this prior knowledge effectively...