# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
###### Abstract
Cycle-Dehaze [21] provides unpaired hazy and clear images, which are fed into a cycle adversarial network for training. Additionally, it introduces a cycle perceptual consistency loss function to address network optimization and evaluation. GFN-Net [22] divides its architecture into feature extraction modules and restoration modules. GCANet [23] was pioneering in employing smooth dilated convolution to tackle grid artifacts in the image dehazing process. EDN-GTB [24] takes both the hazy image and dark channel transmission map as inputs for its U-Net-based model with spatial pyramid pooling modules during the training phase. AECR-NET [25] incorporates contrast regularization by treating haze-free images as positive samples while considering hazy images as negative samples, respectively. AECR-NET approximates restored images towards positive samples while moving away from negative ones, thereby enhancing the transformation capability of models accordingly. The complexity of the image dehazing task lies in the often uneven distribution of haze within images. Faced with the uneven concentration of haze, it necessitates algorithms to pay more attention to areas with higher haze density. Deep-learning algorithms, which rely solely on traditional convolutional neural networks for dehazing, primarily focus on the local features of hazy images and neglect the overall haze density across the image [26]. This bias towards local processing inevitably leads to issues in identifying and dealing with areas of high haze density within the image. Therefore, effectively recognizing and addressing areas with an uneven distribution of haze in hazy images poses a significant challenge for dehazing algorithms [27; 28]. Although the introduction of attention mechanisms can to some extent optimize the algorithm's capture of global features and afford more processing to key features, this strategy also has clear limitations [28; 29]. Attention mechanisms that extract features based on fully connected (FC) layers often lead to a significant increase in the number of model parameters due to the high connectivity of FC layers. This not only exacerbates the algorithm's time complexity but also its space complexity. Moreover, an excessive number of parameters increases the risk of overfitting, leading to insufficient generalizability of the algorithm. In the face of these challenges, dehazing algorithms must balance dehazing effectiveness with reduced demands on computational resources. Considering that FFA-Net still fails to completely eliminate haze from images, particularly in areas of large, similarly colored blocks where the processed image pixel values are not sufficiently smooth, and further, that the channel attention mechanism employed by FFA-Net involves a large number of parameters and significant computational load, we propose an enhanced dehazing algorithm based on multi-feature fusion (MFID-Net) built upon the framework of FFA-Net to address these limitations. MFID-Net integrates multi-scale features and employs a novel lightweight attention mechanism, which not only reduces the demand for computational resources but also effectively enhances the recognition and capture of global image features. Furthermore, MFID-Net demonstrates exceptional detail restoration capabilities in scenarios with large areas of similar color blocks, such as white walls, skies, and roads. The pixel values in these areas are smoother after restoration, reducing the occurrence of black blocks and ghosting. Figure 1 clearly demonstrates the comparative effectiveness of sky scene processing, showcasing the distinct advantage of MFID-Net in handling such specialized scenarios. To effectively focus on the global contextual information of hazy images while reducing the algorithm's computational resource requirements, we introduce a new lightweight gated channel attention (LGCA) mechanism.