Nighttime Image Dehazing Based on Variational Decomposition Model

Yun Liu, Zhongsheng Yan, Aimin Wu, Tian Ye, Yuche Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 640-649


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 L1 norm to constrain the structure component, adopt L0 sparsity term to enforce the piece-wise continuous of the detail layer, and use L2 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.

Related Material

@InProceedings{Liu_2022_CVPR, author = {Liu, Yun and Yan, Zhongsheng and Wu, Aimin and Ye, Tian and Li, Yuche}, title = {Nighttime Image Dehazing Based on Variational Decomposition Model}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {640-649} }