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Self-Parameter Distillation Dehazing
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AI
DOI：10.1109/TIP.2022.3231122.png)
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Abstract
En 中文
In this paper, we propose a novel dehazing method based on self-distillation. In contrast to conventional knowledge distillation approaches that transfer large models (teacher networks) to small models (student networks), we introduce a single knowledge distillation network that transfers network parameters to itself for dehazing. In the early stages, the proposed network transfers scene content (identity) information to the next stage of itself using haze-free data. However, in the later stages, the network transfers haze information to itself using haze data, enabling the accurate dehazing of input images using scene information from the early stages. In a single network, parameters are seamlessly updated from extracting global scene features to dehazing the scene. During the training, forward propagation acts as a teacher network, whereas backward propagation acts as a student network. The experimental results demonstrate that the proposed method considerably outperforms other state-of-the-art dehazing methods.
Keywords:
Knowledge engineering
Training
Task analysis
Atmospheric modeling
Feature extraction
Backpropagation
Deep learning
Dehazing
self-distillation
deraining
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
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