# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
###### Abstract  
Numerous single-image dehazing algorithms have been developed, employing a spectrum of techniques ranging from intricate physical computations to state-of-the-art deep-learning methodologies. However, conventional deep-learning approaches, particularly those based on standard convolutional neural networks (CNNs), often result in the persistence of residual fog patches when applied to images featuring high fog concentration or heterogeneous fog distribution. In response to this challenge, we propose an innovative solution known as the multi-feature fusion image dehazing network (MFID-Net). This approach employs an end-to-end methodology to directly capture the mapping relationship between hazy and fog-free images. Central to our approach is the introduction of a novel multi-feature fusion (MF) module, strategically designed to address channel and pixel characteristics in regions with uneven or high fog concentrations. Notably, this module achieves effective haze reduction while minimizing computational resources, thereby mitigating the issue of residual fog patches. Experimental results underscore the superior performance of our algorithm compared to similar dehazing methods, as evidenced by higher scores in structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and computational velocity. Moreover, MFID-Net exhibits significant advancements in restoring details within expansive monochromatic areas, such as skies and white walls. deep learning; dehazing; attention mechanism +
Footnote †: journal: Medical Image Analysis  
et al. [14] utilized the polarization properties of light to capture polarized components in two different directions from hazy images, calculating their difference to eliminate scattered light and haze. However, this approach is limited to scenes with polarizing properties. The median filter method is applied for transmittance estimation in ASM [15], but it may obliterate details smaller than the filter's aperture size, resulting in diminished resolution and loss of detail in restored images. The dark channel prior (DCP) dehazing algorithm introduced by [PERSON] et al. [16] relies on the discovery that outdoor haze-free images have at least one RGB channel with the lowest pixel values. Nevertheless, DCP may cause color distortion in large monochromatic areas such as the sky or white walls. Color prior attenuation (CPA) [17] applies color prior values to each pixel's color value in hazy images, attenuating fog accordingly. However, these algorithms heavily depend on specific prior knowledge that may not be universally applicable, leading to suboptimal dehazing results in certain scenarios (e.g., nighttime scenes, indoor environments, sky regions). Deep-learning-based image dehazing algorithms adaptively adjust parameters according to input images, avoiding the complexities of modeling and parameter estimation. These algorithms are generally divided into two types: parameter estimation-based algorithms and direct resolution algorithms. In the former, networks estimate parameters such as transmittance and atmospheric light, followed by inverse computation using ASM to obtain haze-free images. For instance, Dehaze-Net [18] employs CNNs to learn the mapping relationship between hazy images and transmittance. AOD-Net [13] reconstructs the ASM by combining atmospheric light with transmittance into a single variable to reduce the loss of image features. DCPDN-Net [19], employing two subnetworks for the sequential learning of ambient light and transmittance, gradually optimizes different components of the network. However, DCPDN-Net may introduce image artifacts. The latter type of algorithms directly trains networks with foggy images to seek optimal dehazing parameters. For example, [PERSON] et al. [20] proposed the concept of perceptual dehazing, which utilizes a physical model to decouple and reconstruct the hazy image into hidden feature values. Subsequently, a multiscale adversarial network is employed to restore the haze-free image.