WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement
and Beyond
- URL: http://arxiv.org/abs/2308.00931v1
- Date: Wed, 2 Aug 2023 04:17:35 GMT
- Title: WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement
and Beyond
- Authors: Zengxi Zhang, Zhiying Jiang, Jinyuan Liu, Xin Fan, Risheng Liu
- Abstract summary: Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice.
We propose a normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow.
Considering the differentiability and interpretability, we incorporate the prior into the data-driven mapping procedure.
- Score: 52.27796682972484
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Underwater images suffer from light refraction and absorption, which impairs
visibility and interferes the subsequent applications. Existing underwater
image enhancement methods mainly focus on image quality improvement, ignoring
the effect on practice. To balance the visual quality and application, we
propose a heuristic normalizing flow for detection-driven underwater image
enhancement, dubbed WaterFlow. Specifically, we first develop an invertible
mapping to achieve the translation between the degraded image and its clear
counterpart. Considering the differentiability and interpretability, we
incorporate the heuristic prior into the data-driven mapping procedure, where
the ambient light and medium transmission coefficient benefit credible
generation. Furthermore, we introduce a detection perception module to transmit
the implicit semantic guidance into the enhancement procedure, where the
enhanced images hold more detection-favorable features and are able to promote
the detection performance. Extensive experiments prove the superiority of our
WaterFlow, against state-of-the-art methods quantitatively and qualitatively.
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