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Dynamic patch-level contrastive learning for image dehazing
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AI
DOI：10.1016/j.patcog.2026.113529.png)
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Abstract
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• A patch-based contrastive learning method is proposed for image dehazing. • The method dynamically selects positive and negative samples based on fog density. • Grayscale images are used to reduce atmospheric light interference. • Both local and global features are captured to preserve image details. • The approach achieves strong results on synthetic and real-world datasets.
Keywords:
contrastive learning
image dehazing
patch-based method
fog density
feature preservation
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7.6
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