Abstract:
In single image dehazing, image noise causes depth-dependent visual artifacts. Most dehazing methods use a two-step restoration strategy, which produces inaccurate transmission maps and poor scene radiance for noisy and hazy inputs.
We propose a variational model to recover the transmission map and scene radiance from a single image. We propose a transmission-aware non-local regularization to suppress noise and preserve fine details in the recovered image.
A semantic-guided regularization smooths the transmission map while maintaining depth inconsistency at object boundaries to improve transmission estimation accuracy. An alternating scheme optimizes the transmission map, scene radiance, and segmentation map.
The proposed algorithm outperforms state-of-the-art dehazing methods on noisy and hazy images in extensive synthetic and real-world experiments.
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