# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 2 Related Work
### Image Dehazing  
In convention, haze is viewed as a natural phenomenon that occurs when light interacts with particles in the atmosphere. The scattering of light by particles in the atmosphere causes the light to be scattered in all directions, resulting in a decrease in the contrast of the image [12]. In 1976, [PERSON] [13] first proposed the basic Atmospheric Scattering Model (ASM) theory to approximately explain the principles of haze formation. Then, in 2003, [PERSON] [14] and [PERSON] [15] further developed the ASM that is currently widely used. In the early stages, dehazing algorithms were similar to other image restoration tasks such as denoising and deblurring, relying on prior-based approaches. These methods were based on assumptions and prior knowledge derived from statistical analysis of a large number of degraded images' physical properties, which were then used to implement the algorithm's effectiveness. For example, [PERSON]. [16] proposes the Dark Channel Prior (DCP)method. It assumes the minimum intensity value in one local window of a three-channel image intended to be zero in the haze-free image, and based on it the medium transmission map, as well as the global atmospheric light \(A\), is estimated. [PERSON]. [17] introduces a single-image dehazing method utilizing color-lines regularity and an augmented Markov random field model, significantly improving transmission accuracy and haze-free radiance recovery. [PERSON]. [18] introduces the Rank-one Prior (RP) method, which proposes that the medium transmission map \(t\) as a rank-one matrix can be estimated by intensity projection strategy. [PERSON]. [19] builds a color attenuation prior to haze removal from a single image, enabling the creation of a linear model for scene depth estimation and significantly enhancing dehazing efficiency and effectiveness compared to state-of-the-art algorithms. However, these methods are not robust enough to handle complex scenes and are sensitive to the choice of parameters. In recent years, with the development of deep learning, the application of neural networks in image dehazing has become increasingly popular. Dehazenet [20] firstly carries out convolutional neural networks(CNNs) to forecast \(t\). DCPND [21] simultaneously predicts \(A\) and \(t\) by utilizing CNNs. AOD-Net [22] performs a dehazing network that simplifies the image dehazing process by directly generating clean images without separately estimating the transmission matrix and atmospheric light, demonstrating superior performance and fast processing speed. [PERSON]. [23] proposes an end-to-end trainable neural network for single-image dehazing that leverages a fusion strategy to blend multiple derived inputs from the original hazy image, eliminating the need to explicitly estimate transmission and atmospheric light. 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 which is vital for image dehazing. Some works on account of Transformers have also been introduced. Such as DeHamer [24] combines the CNN and conventional Transformer for image dehazing, DehazeFormer [10] utilizes the modified Swin Transformer [26] key structure enabling the elasticity for image dehazing. [PERSON]. [25] introduces an Image Processing Transformer (IPT), a pre-trained model designed specifically for low-level vision tasks such as denoising, super-resolution, and deraining, leveraging a large-scale dataset and a multi-head, multi-tail architecture to achieve state-of-the-art performance across various benchmarks. This method is based on a transformer architecture and achieves excellent image restoration by processing the global and wide-range associative information in the image, refining the results from coarse to fine. Also, there exist works like MixDe