# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
###### Abstract
LGCA utilizes regularization components and learnable parameters instead of traditional FC layers to simulate the interaction between channels, enabling better capture of global contextual information and extraction of image features in areas with high haze density. Experimental results demonstrate that LGCA  
Figure 1: An example of image dehazing when processing sky scenes. achieves significant improvements in dehazing performance for hazy images, while also reducing dependence on computational resources. A gating mechanism plays a pivotal role in neural networks by effectively regulating the flow and selection of information between models, thereby significantly enhancing algorithmic accuracy and generalization capability [30]. The effectiveness of a gating mechanism hinges on its precise control of input-output ratios and its capacity to filter out irrelevant information while reinforcing important information. This capability enables the gating mechanism to finely adjust information, empowering the algorithm to adaptively select and integrate useful features. With this consideration, we propose a novel gating mechanism \(1+tanh(x)\), designed to control the feature flow between channels and pixels, adaptively filtering and integrating valuable features to enhance the dehazing effect of the algorithm. Through this approach, MFID-Net exhibits improved performance and robustness. The key contributions of this work are listed below. * Building upon FFA-Net, we proposed MFID-Net to enhance the algorithm's capability in detail recovery, particularly when addressing complex scenes containing extensive structural color blocks, such as white walls and sky. Table 1 presents a comparative analysis based on the FFA-Net architecture, which includes modifications such as replacing its pixel attention (PA) mechanism with the GPA, substituting the conventional channel attention (CA) mechanism with the LGCA, and integrating these improvements to develop the MFID-Net. The results indicate that MFID-Net not only achieves significant improvements in dehazing effects but also effectively reduces the algorithm's parameter count. An innovative multi-feature fusion (MF) module is proposed, focusing on the efficient integration of channel and pixel features within images. The MF module ingeniously combines three distinct components to work collaboratively, thereby significantly enhancing the algorithm's expressive capability in image processing. * Proposed LGCA, utilizing \(\ell_{1}\)-Norm and \(\ell_{2}\)-Norm methods in lieu of FC layers to reduce model parameters and alleviate computational resource requirements. Furthermore, LGCA allows the algorithm to concurrently focus on both local and global information, enhancing feature extraction capabilities. * A novel gated pixel attention (GPA) module has been introduced, aimed at enhancing the cooperation and competition among pixel features, thereby significantly improving the flexibility and adaptability of dehazing algorithms. This innovative GPA module demonstrated superior dehazing performance in processing foggy images. The remaining sections of this work are structured as follows. Section 2 provides an overview of the existing literature in the field of image dehazing. Section 3 elaborates on the implementation methods and details of the proposed model. Section 4 presents experimental results and facilitates discussion. Finally, Section 5 offers a concise summary encompassing this work.