Single Image Dehazing Using Fuzzy Region Segmentation and Haze Density Decomposition

Tao Li, Yun Liu, Wenqi Ren, Babak Shiri, Weisi Lin

Abstract: Images captured under haze weather conditions usually suffer from visual quality degradations, such as blurred details, faded colors, and decreased saturation. Existing physics-based dehazing methods mainly have two drawbacks: 1) the atmospheric light is treated as a constant for the entire image, and 2) pixel- or patch-based strategies are employed to estimate the model parameters, resulting in inaccurate haze density estimations. Therefore, these methods may lead to over-dehazing or under-dehazing due to insufficient utilization of features from regions with similar haze densities. To address these issues, a novel single image dehazing framework based on fuzzy region segmentation and haze density decomposition is proposed. Specifically, a region-based physical model that considers the non-uniform atmospheric light is first constructed based on the classic atmospheric scattering model. Then, a fuzzy segmentation algorithm is improved to divide the input hazy image into several separate regions. Subsequently, we formulate a simple linear relationship between the atmospheric light and brightness to estimate region-based atmospheric light. On the other hand, we develop a novel haze density decomposition algorithm based on boundary constraints to separate the atmospheric veil into two components: thin part and dense part. Three haze-related features, contrast, gradient and clarity, are extracted from the input hazy image to construct weight maps and a multi-scale fusion is further exploited to combine weight maps and boundary veils to acquire the refined atmospheric veil. Finally, the model inversion is performed to acquire the haze-free result. Experiments on six diverse hazy datasets demonstrate that the proposed algorithm outperforms several state-of-the-art dehazing methods in both visual quality and objective evaluation.

External IDs:dblp:journals/tcsv/LiLRSL25