Abstract
Most of existing dehazing algorithms are unable to deal with nighttime hazy scenarios well due to complex degraded factors such as non-uniform illumination, low light and glows. To obtain high-quality image under nighttime haze imaging conditions, we present an effective single nighttime image dehazing framework based on a variational decomposition model to simultaneously address these undesirable issues. First, a variational decomposition model consisting of three regularization terms is proposed to simultaneously decompose a nighttime hazy image into a structure layer, a detail layer and a noise layer. Concretely, we employ ℓ<inf>1</inf> norm to constrain the structure component, adopt ℓ<inf>0</inf> sparsity term to enforce the piece-wise continuous of the detail layer, and use ℓ<inf>2</inf> norm to separate the noise layer. Next, the structure layer is recovered by means of inversing the physical model and the detail layers are revealed in a multi-scale gradient enhancement manner. Finally, the dehazed structure layer and the enhanced detail layers are integrated into a haze-free image. Experimental results show that the proposed framework achieves superior performance on nighttime haze removal and noise suppression compared with several state-of-the-art dehazing techniques.
Published Version
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