# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 2 Related Works
### Deep-Learning-Based Methods  
With the availability of large scale datasets and the development of deep neural networks, data-driven deep-learning-based methods have achieved promising results in single-image dehazing. At first, deep learning was used to estimate variables of the atmospheric scattering model in Equation (1) or certain derived variables [24; 25; 26; 27]. For example, [PERSON] et al. [24] proposed DehazeNet, which takes in a hazy image and outputs its transmission map. Apart from the transmission map, the global atmospheric light also needs to be estimated separately in order to recover a haze-free image via the atmospheric scattering model. [PERSON] et al. [25] reformulated the atmospheric scattering model and designed the all-in-onedehazing network (AOD-Net) to estimate a \(K\) variable that effectively combines the global atmospheric light and transmission map into one. Later on, end-to-end convolutional neural networks (CNNs) were devised to directly estimate the haze-free image from a haze-degraded image [12; 13; 14; 15; 16; 17; 18; 19]. [PERSON] et al. [12] proposed GridDehazeNet, which implements a novel attention-based multi-scale estimation on a grid network for single-image dehazing. [PERSON] et al. [13] presented the feature fusion attention network (FFA-Net) in which features at different levels are fused by an attention-based feature fusion structure combining channel attention with a pixel attention mechanism. While FFA-Net considers different channel-wise features and an uneven haze distribution, it carries out convolutional operations at the resolution of the original image, resulting in a large amount of calculation. [PERSON] et al. [14] designed the multi-scale boosted dehazing network (MSBDN) with dense feature fusion based on the U-Net architecture, which incorporates the strengthen-operate-subtract boosting strategy in the decoder of the model. [PERSON] et al. [15] proposed a fast deep multi-patch hierarchical network (DMPHN) to restore non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image. [PERSON] et al. [19] introduced a two-branch neural network to separately deal with non-uniformly distributed haze and the limited data challenge and fuse features from these two branches. [PERSON] et al. [16] designed a generative adversarial network with residual guided filters that effectively obtains the contour information of a hazy image. Apart from designing more powerful CNNs, advanced learning techniques are also exploited for single-image dehazing. For example, contrastive learning is used to exploit both the information of hazy images and clear images as negative and positive samples [18]. While achieving promising dehazing results, these networks often have a large number of parameters or perform poorly on nonhomogeneous haze situations in the real world. Unlike the representative end-to-end CNN-based dehazing techniques, the proposed method takes into consideration both a lightweight network design and the ability to cope with the nonuniformity of haze. Specifically, we use multi-scale convolutions and an improved attention mechanism to remove dense haze and thin haze simultaneously. Recently, visual transformers have also been utilized for image dehazing due to their capabilities for global modeling [20; 28; 29; 30; 31]. To take the advantage of both visual transformers and CNNs, [PERSON] et al. [28] proposed a transformer-convolution fusion dehazing network. [PERSON] et al. [20] brought a haze density-related prior into the transformer via a novel transmission-aware 3D position-embedding module and modulated the CNN features via learning modulation matrices conditioned on transformer features, instead of simple feature addition or concatenation. However, this transformer relies on large-scale training data for optimal performance. [PERSON] et al. [29] proposed a transformer-based channel attention module combined with a spatial attention module to enhance a CNN-based backbone network.