Uncertainty-Driven Dehazing Network

Deep learning has made remarkable achievements for single image haze removal. However, existing deep dehazing models only give deterministic results without discussing their uncertainty. There exist two types of uncertainty in the dehazing models: aleatoric uncertainty that comes from noise inherent in the observations and epistemic uncertainty that accounts for uncertainty in the model. In this paper, we propose a novel uncertainty-driven dehazing network (UDN) that improves the dehazing results by exploiting the relationship between the uncertain and confident representations. We first introduce an Uncertainty Estimation Block (UEB) to predict the aleatoric and epistemic uncertainty together. Then, we propose an Uncertainty-aware Feature Modulation (UFM) block to adaptively enhance the learned features. UFM predicts a convolution kernel and channel-wise modulation coefficients conditioned on the uncertainty weighted representation. Moreover, we develop an uncertainty-driven self-distillation loss to improve the uncertain representation by transferring the knowledge from the confident one. Extensive experimental results on synthetic datasets and real-world images show that UDN achieves significant quantitative and qualitative improvements, outperforming state-of-the-arts.

Authors

• Ming Hong
• Jianzhuang Liu Shenzhen Institute of Advanced Technology
• Cuihua Li
• Yanyun Qu School of Informatics, Xiamen University Institute of Artificial Intelligence, Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing,Ministry of Education of China, Xiamen University

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