# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
### End-to-End Deep Learning Defogging Approach  
In order to solve the problems of the inaccurate estimation of atmospheric light values, we conducted the separate estimation of the haze image transmittance maps and the cumulative effect of errors brought by atmospheric light. Recently, many scholars have used convolutional neural networks to simultaneously estimate the atmospheric light and transmittance of the haze images and directly output the defogged images, thus realizing an \"end-to-end\" deep learning defogging method that obtains defogging maps from the fog maps in one step [10]. [PERSON] et al. [11] proposed the AOD-Net network, which assumes the atmospheric light value and transmittance as a parameter K, and then uses a large number of cascaded convolutional layers and a neural network constructed across the hierarchy to estimate the K value, thus completing end-to-end image defogging. The method improved the quality of the defogged images while avoiding the accumulated errors in the recovered images, but the network model was limited to a shallow structure and failed to learn the features of the fogged images well, which affected the quality of the recovered images. [PERSON] et al. [12] proposed an end-to-end gated contextual aggregation network to generate clear and fog-free images directly. The network utilized a smooth expansion technique that eliminates grid artifacts and fuses different levels of features through gated subnetworks. The input fogged image in this network was encoded into a feature map by an encoder, and the image was enhanced by aggregating information from nearby regions and fusing different levels of features. However, the current end-to-end defogging method based on convolutional neural networks still has some limitations, although it is free from the constraints of atmospheric scattering models. After the action of shallower convolutional layers, each pixel point of the feature image is only a feature extraction of the local information of the original image, the size of the convolutional kernel determines the scope of the local weighting of the image, and the image is rich in detailed information, but the image has little contextual information. Convolutional operations are good at extracting local features, but it is difficult to capture the global representation, and it is crucial to obtain a global perception when performing image recovery. To alleviate these limitations, it is necessary to obtain a larger field of perception, and the classical way to increase the field of perception is to downsample the image or feature map to obtain multi-scale information. However, downsampling may lose some useful detailed information that cannot be recovered by upsampling. The use of larger convolutional kernels or more convolutional layers can also increase the perceptual field, but at the same time, the computational effort increases significantly. Dilated convolution can expand the perceptual field without increasing the computational cost, but as the dilation rate increases, the information of neighboring elements of the convolution kernel varies widely, leading to grid artifacts in the defogged results. Attentional mechanisms can quickly capture long-range dependencies by calculating the relationship between two locations in the channel and space to obtain a larger receptive field. [PERSON]'s team proposed the SENet attention module, which uses global average pooling to aggregate the global contexts to make them globally attentional information, and then reweighting the feature channels to amplify the weights of important feature maps [13]. However, SENet only focuses on the synthesis of information within the channel and does not take into account the importance of adjacent channel information. \"Attention is all you need\" published by [PERSON] et al., in 2017 introduces the transformer model with self-attention as the basic unit that makes the attention mechanism really successful [14].