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I'm training a CNN with images which have lots of horizontal black lines (due to the nature of the sensor). I'm thinking in removing this artifacts by some kind of preprocessing (interpolation, median filters...). The thing is: does it make sense, since the CNN tries to apply optimal filtering? (if some 2D filtering is intended to be done just before the CNN, is just adding a deterministic layer at the beginning of the net...)

ignatius
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That is an empirical question which can be answered through cross validation.

Consider keeping or removing sensor artifacts a hyperparameter. See if the different values of that hyperparameter impact performance on an evaluation metric on a hold-out dataset.

Brian Spiering
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