Detecting stitching artifacts in marker data analysis is challenging. Attached below is an example of how the stitching effect looks (on the right hand side) As you can see, some markers show stitching patterns (the block/square like structures or horizontal/vertical lines) which is problematic for downstream analysis.
I would like to know how I can detect and/or correct stitching for these types of images. My current approach involves applying the Fourier transformation in Python to the intensity of each marker, however it has yielded mixed results.
Are there any recommendations or alternative methods that might enhance this detection/correction process? Is there any code that may bolster the Fourier transformation in my favor?
Fourier Transformation Example:
So far, I have attempted:
- Edge detection
- Average intensity of inverse Fourier
- Distribution of frequency component of the Fourier transformation
- Histogram analysis (raw images only)
- Plotting phase vs amplitude in Fourier transformation
We were expecting to see relatively consistent results of what stitching vs nonstitching effect would look like. I.e. in stitching, perhaps we would see more blob-like structures, vs in nonstitching, we would see a homogenous grating patttern. Or, in stitching, we would see a skewed distribution, vs in nonstitching, we would see a normal distribution. Generally, we are hoping to detect a consistent pattern, but have failed so far.