# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
###### Abstract  
In recent years, numerous single-image dehazing algorithms have made significant progress; however, dehazing still presents a challenge, particularly in complex real-world scenarios. In fact, single-image dehazing is an inherently ill-posed problem, as scene transmission relies on unknown and nonhomogeneous depth information. This study proposes a novel end-to-end single-image dehazing method called the Integrated Feature Extraction Network (IFE-Net). Instead of estimating the transmission matrix and atmospheric light separately, IFE-Net directly generates the clean image using a lightweight CNN. During the dehazing process, texture details are often lost. To address this issue, an attention mechanism module is introduced in IFE-Net to handle different information impartially. Additionally, a new nonlinear activation function is proposed in IFE-Net, known as a bilateral constrained rectifier linear unit (BCReLU). Extensive experiments were conducted to evaluate the performance of IFE-Net. The results demonstrate that IFE-Net outperforms other single-image haze removal algorithms in terms of both PSNR and SSIM. In the SOTS dataset, IFE-Net achieves a PSNR of 24.63 and an SSIM value of 0.905. In the ITS dataset, the PSNR value is 25.62, and the SSIM value reaches 0.925. The quantitative results of the synthesized images are either superior to or comparable with those obtained via other advanced algorithms. Moreover, IFE-Net also exhibits significant subjective visual quality advantages. BCReLU; feature extraction; convolutional neural network; single-image dehazing +
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where \(d(x)\) is the distance from the scene point to the camera, and \(\beta\) represents the attenuation coefficient of the atmosphere. From Equation (1), it can be seen that the dehazing process requires the accurate estimation of the transmission map and atmospheric light. A small portion of research mainly focuses on estimating atmospheric light [8; 9; 10; 11; 12], but the accuracy of the atmospheric light obtained will directly affect the results after dehazing and excessive errors will lead to a decrease in the dehazing performance on the image. Alternative other algorithms focus more on accurately estimating transmission maps, and the estimation of a transmission map mainly falls into two categories: prior-based methods [13; 14] and learning-based methods [15; 16]. In order to compensate for information loss during image processing, some methods use different priors to obtain atmospheric light and transmission maps. For example, [PERSON] et al. [17] proposed a non-local prior-based dehazing algorithm based on the assumption that the colors of clean images are well approximated by different colors. Based on the difference in brightness, the saturation of blurred images is blurred, and color attenuation prior (CAP) [18] is proposed to estimate scene depth. The image prior obtained using prior-based algorithms can easily be inconsistent with practice, which may lead to incorrect transmission approximations. Learning-based methods are effective and superior to prior based-algorithms, exhibiting significant performance improvements. In [19], two subnetworks were designed to estimate the transmission map and atmospheric light, respectively. In [20], the authors created three different images from the hazy image and fused the results of the three images after dehazing. However, deep learning-based methods require training on a large number of real hazy images and their corresponding images without haze. The methods of estimating atmospheric light and transmission maps separately have made significant progress, but both have limitations. On the one hand, the inaccurate estimation of transmission maps may lead to low image quality; on the other hand, the separate estimation of atmospheric light and transmission maps leads to difficulties in finding the inherent relationship between them. In order to find the intrinsic relationship between the parameters of Equation (1), [PERSON] et al.