Hello all,
At the moment, I am working on a project about the failure/fault detection on Photovoltaic Module based on Deep Learning using electroluminescence (EL) images. In this project, my proposed method of data reprocessing before applying to the Deep Learning model is inspired on what is currently used in Healthcare for X-ray images: for X-ray images, the reprocessing method is first applying the Intensity Normalization then Contrast Limited Adaptive Histogram Equalization.
My question is: how do I compare between the EL images and the X-ray i.e. compare images from different modalities? The results of this comparison can give me a first glance whether this proposed method is viable.
When you have images obtained with different imaging modalities, you need to normalize them in terms of coordinates and image transformations (e.g., translation, rotation, and scaling).
Your images may have some vision artifacts that can impair the comparison.
This is called image pre-processing and it is frequently underestimated in designs.
Regards
I would first check for what a "human-vision" system would do when comparing these two different modalities and then try to mimic that by using an algorithm (if possible). By the "human-vision" system I mean a physician/radiologist.
compares two images and returns a value that tells you how visually similar they are
When you have images obtained with different imaging modalities, you need to normalize them in terms of coordinates and image transformations (e.g., translation, rotation, and scaling).
Your images may have some vision artifacts that can impair the comparison.
This is called image pre-processing and it is frequently underestimated in designs.
Regards
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