# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 2 Related Work
### Image Dehazing
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Citation** & **Method** & **Category** & **Characteristics** \\ \hline
[16] & Dark Channel Prior (DCP) & & Based on assumptions and prior knowledge derived from statistical analysis of a large number of degraded images’ physical properties. It requires multiple iterations of calculations, can only handle images under specific hypothetical conditions, and has high computational complexity with limited satisfactory results. \\ \hline
[17] & \
\begin{tabular}{c} Color-lines regularity and an augmented \\ Markov random field model \\ \end{tabular} & Prior-based &
\begin{tabular}{c} Fixed on assumptions and prior knowledge derived from statistical analysis of a large number of degraded images’ physical properties. It requires multiple iterations of calculations, can only handle images under specific hypothetical conditions, and has high computational complexity with limited satisfactory results. \\ \end{tabular} \\ \hline
[18] & Rank-one Prior (RP) & & With a sufficient amount of synthetic image pairs, these can achieve superior dehazing results over traditional methods. However, due to the limited receptive field of CNN, which only captures spatially local features, it is challenging to obtain long-range dependencies of pixels. \\ \hline
[20] & estimating transmission matrix \(t\) by CNN &
\begin{tabular}{c} With a sufficient amount of synthetic image pairs, these can achieve superior dehazing results over traditional methods. However, due to the limited receptive field of CNN, which only captures spatially local features, it is challenging to obtain long-range dependencies of pixels. \\ \end{tabular} \\ \hline
[21] & \
\begin{tabular}{c} end-to-end neural network & &
\begin{tabular}{c} Compared to traditional CNN algorithms, this method achieves better image restoration metrics and generalization ability through multi-head self-attention computation. However, traditional Transformer based image dehazing methods are computationally intensive and a higher risk of overlocalization. \\ \end{tabular} \\ \hline
[25] & Image Processing Transformer (IPT) & &
\begin{tabular}{c} Compared to traditional CNN algorithms, this method achieves better image restoration metrics and generalization ability through multi-head self-attention computation. However, traditional Transformer based image dehazing methods are computationally intensive and a higher risk of overlocalization. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of image dehazing methods. hazeNet [11] mix multiple attention mechanisms, such as channel attention, pixel attention, etc., diversiform kernel sizes, to achieve state-of-the-art (SOTA) performance in image dehazing. Although these algorithms achieve better hazing removal results compared to classical methods based on prior estimation, they still face two challenges: Firstly, they may not adapt well to complex real-world hazed image processing, and in some cases, they may even struggle to effectively restore certain artificial hazed images. Secondly, despite the proposal of diverse network modules and ongoing technological iterations, the parameter volume of network models continues to increase. In our work, we aim for the network to maintain a relatively small parameter volume while still delivering excellent hazed image restoration results.