Haze Removal via Regional Saturation-Value Translation and Soft Segmentation

• URL: http://arxiv.org/abs/2403.12054v1
• Date: Sun, 7 Jan 2024 07:52:50 GMT
• Title: Haze Removal via Regional Saturation-Value Translation and Soft Segmentation
• Authors: Le-Anh Tran, Dong-Chul Park,
• Abstract summary: This paper proposes a single image dehazing prior, called Regional Saturation-Value Translation (RSVT) The RSVT prior is developed based on two key observations regarding the relationship between hazy and haze-free points in the HSV color space. Experimental results on various synthetic and realistic hazy image datasets demonstrate that the proposed scheme successfully addresses color distortion issues.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a single image dehazing prior, called Regional Saturation-Value Translation (RSVT), to tackle the color distortion problems caused by conventional dehazing approaches in bright regions. The RSVT prior is developed based on two key observations regarding the relationship between hazy and haze-free points in the HSV color space. First, the hue component shows marginal variation between corresponding hazy and haze-free points, consolidating a hypothesis that the pixel value variability induced by haze primarily occurs in the saturation and value spaces. Second, in the 2D saturation-value coordinate system, most lines passing through hazy-clean point pairs are likely to intersect near the atmospheric light coordinates. Accordingly, haze removal for the bright regions can be performed by properly translating saturation-value coordinates. In addition, an effective soft segmentation method based on a morphological min-max channel is introduced. By combining the soft segmentation mask with the RSVT prior, a comprehensive single image dehazing framework is devised. Experimental results on various synthetic and realistic hazy image datasets demonstrate that the proposed scheme successfully addresses color distortion issues and restores visually appealing images. The code of this work is available at https://github.com/tranleanh/rsvt.

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