A differentiable histogram-matching preprocessing whose target distribution is learned end-to-end from normal-weather images improves classification accuracy on unseen fog, rain, sand, and snow images.
1 shows a block diagram of the proposed HM
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Rethinking Image Histogram Matching for Image Classification
A differentiable histogram-matching preprocessing whose target distribution is learned end-to-end from normal-weather images improves classification accuracy on unseen fog, rain, sand, and snow images.