# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
###### Abstract
[21] first proposed a dehazing model that does not estimate \(\alpha\) and \(t(x)\). This model directly generates clean images from blurred images, rather than relying on any separate intermediate parameter estimation steps. Recently, many methods have used end-to-end learning instead of atmospheric scattering models to directly obtain clean images from networks [22; 23; 24; 25]. Another widely used method tends to predict the residual of potential haze-free images or haze-free images relative to hazy images, as they often achieve better performance [26; 27; 28; 29; 30]. Although these recent dehazing methods have made significant progress, due to the complex haze distribution and the difficulty in collecting image pairs during the training process, it is easy to lose image details during the dehazing process using limited a dataset. Due to the difficulty in collecting image pairs during the training process, IFE-Net uses end-to-end models, adaptively learns network features, and adopts multiscale feature extraction to better extract information. In addition, parallel convolutional layers of different sizes are used to extract features from input images of different scales [31; 32]. This feature extraction structure is conducive to preserving more information and reducing the loss of image details. Considering the potential cumulative error caused via the separate estimation of atmospheric light and the transmission map, IFE-Net unifies atmospheric light and transmission maps as one parameter to directly obtain a clean image. In addition, attention mechanisms have been widely applied in the design of neural networks [19; 33; 34; 35; 36], which can provide additional flexibility in the network. Inspired by these works and considering the different weights of features in different regions, a feature attention mechanism module called attention mechanism (AM) is designed in the network, which processes different types of information more effectively. In deep learning networks, the activation function is a nonlinear function that enables neural networks to learn and represent complex patterns and relationships. The selection of the final activation function has a significant impact on the output results of the model, as different activation functions have different characteristics and applicable scenarios. We considered that the output of the last layer of the image after dehazing should have upper and lower boundaries. In IFE-Net, we designed a new activation function called a bilateral constrained rectifier linear unit (BCReLU). The specific details of BCReLU and its comparison with other activation results in the network are detailed in Section 3.2.3. The main contributions are as follows:  
1. IFE-Net directly produces the clean image from a hazy image, rather than estimating the transmission map and atmospheric light separately. All parameters of IFE-Net are estimated in a unified model. 2. We propose a novel attention mechanism (AM) module, which consists of a channel attention mechanism, pixel attention mechanism, and texture attention. This module has different weighted information for different features and focuses more on strong features in areas with thick haze. 3. A bilateral constrained rectifier linear unit (BCReLU) is proposed in IFE-Net. To our knowledge, no one else has proposed BCReLU. Its significance in obtaining image restoration is demonstrated through experiments. 4. The experiments show that IFE-Net performs well both qualitatively and quantitatively. The extensive experimental results also illustrate the effectiveness of IFE-Net.