# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 1 Introduction
This approach not only enhances the details in under-exposed regions but also maintains the overall naturalness of the image [17]. Specifically, in the calculation of the perceived reflectance and perceived light components of an image using Center-Surround Retinex (CSR) models, by adjusting the dynamic range of the perceived illumination component, we were able to enhance the details in dark areas while maintaining the naturalness of the image [18]. This method has shown promising results in improving the visual quality of images with non-uniform illumination. Image restoration defogging approaches are grounded in the physics of aerosol scattering on imaging. An atmospheric scattering model describes this effect, as seen in Figure 1. The characteristics of a clear image are analyzed to estimate the transmission map and at-atmospheric light intensity of a foggy image. Next, the model's inverse process is carried out to recover an image clear of haze. Through a statistical analysis of extensive outdoor clear image datasets, researchers such as [PERSON] et al. observed that in most non-sky patches, at least one RGB channel exhibits very low intensity values, often approaching zero. Dark channel prior [19] (DCP) theory, which is used to create a transmission map and calculate atmospheric light intensity, was put forth in response to this phenomenon. [PERSON] et al. optimized the transmission map obtained from the DCP using a guided filter [20] to provide a refined transmission map. However, this method fails to account for sky regions, leading to inaccurate estimates that cause artifacts like intense halos and color distortion in dehazed images. To address these limitations, numerous researchers have proposed methods focusing on refining transmission maps and atmospheric light estimation. A haze reduction approach based on global illumination [21] adjustment was presented by [PERSON] et al. to lessen the effects of background interference on atmospheric light and transmission map estimation. A vector orthogonality-based technique [22] for determining the atmospheric light vector's amplitude and direction was presented by [PERSON] et al. In order to accomplish accurate transmission map estimation, [PERSON] et al. applied gamma correction [23] to the transmission map that was previously acquired from the dark channel. They then fused the original and corrected transmission maps using a weighted averaging image fusion algorithm. In order to obtain accurate transmission maps, [PERSON] et al. combined the benefits of the image enhancement and picture restoration defogging procedures by introducing a Gaussian-weighted image fusion method [24]. In order to restore transmission maps and atmospheric light intensity, [PERSON] et al. [25] suggested a Rayleigh scattering and adaptive color compensation method that uses chromaticity and luminance variations in areas where the dark channel prior is ineffective for regional segmentation. [PERSON] et al. proposed a pixel-level transmission estimation technique based on estimated radiance patches in order to address the problem of outliers brought on by depth discontinuities during the refinement of initial transmission maps [26]. [PERSON] et al. [27] employed a boundary-constrained method for coarse transmittance estimation, followed by refinement using a weighted L1-norm regularization. [PERSON] et al. [28] presented a haze removal method based on independent component analysis; however, it is ineffective under dense haze conditions. A quick defogging technique based on the atmospheric dissipation coefficient was presented by [PERSON] et al. [29]. Although this technique offers high processing speed, it requires extensive parameter tuning, and its performance is highly sensitive to parameter changes, reducing its flexibility. Additionally, it can cause brightness reduction and halo artifacts at depth edges in dehazed images. The color attenuation prior (CAP) [30] was created from the observation by [PERSON] et al.