I've been experimenting with various methods to detect defects in printed images. Features that are qualified as defects may include scratches, discoloration, etc.
I'm not very experienced in image processing and not sure if my approach would be acceptable.
There are several approaches that I've tried:
Given two images (one good, the other one is defect)
- Threshold and blur
- Run Canny edge detection
- Remove noises using
- XOR the two images and use
SIFTto find outstanding features that may be classified as a defect.
It works very well - in very limited cases.
Seems like this method is used/proposed in industry a bit (?) as seen in this paper. But the problem with it is that if you have two images that are misaligned to the slightest bit, then you would get a problem because all your XOR result in an XOR-ed image that is very wrong. So to me it doesn't seem like an acceptable solution, since it is impossible to perfectly align two images every time (or I could try to realign the images).
How should I approach this. Is there a method that is similar to XOR-ing that is more tolerant to subtle differences? Or perhaps, a totally different approach.
Break up both images into chunks of n by n pixels. Compute and compare the histogram of each chunk of pixels broken up from image A to image B. Even if one of the images is blurred a little, the histogram should be still be similar (not sure if this is a sound assumption). If any chunk contains dark or white spots, this should flag that chunk for further inspection.
Detects darker font color
Detects eyebrows that aren't supposed to be there
Detects faded/lighten area