Single Image Dehazing Using Fuzzy Region Segmentation and Haze Density Decomposition

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.