We consider the application of wavelet transform and neural networks to solving the problem of defect detection (the lack of elements and the presence of adhesions elements) in multi-element photodetectors by processing their images. It is shown that both methods can be successfully applied to the detection of defects. Found that a method based on wavelet transform requires the manual selection of parameters depending on the size of the processed image. Due to the ability of neural networks to learn, a method for the search for defects with neural networks, automatically adapts to the processed image.

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