## 1 Introduction
These models may perform badly under certain lighting conditions, such as strong sunlight and shadows. The models require a large amount of training data, and the decreased size of training data may lead to overfitting and poor generalization ability. Meanwhile, due to the large amount of computing resources and data required to train these models, the costs for deployment can be high. The most undesired part is the incompleteness of fog removal with varied haze densities for the deep-learning-based demisting methods, especially for situations with high haze density, which will be specifically stated in Chapter 3. The traditional defogging algorithm does not have such problems. The dark channel prior (DCP) method proposed by [PERSON] et al. [20] is one of the most famous single-image dehazing methods. DCP imposes the assumption that there exists an extremely dark pixel in a local non-sky patch for every color channel of the image [21]. Due to the computationally expensive drawback of implementing DCP with the soft-matting method [22], some studies [23; 24; 25] have employed guided filtering, bilateral filtering, and edge substitution to replace the soft-matting process. This significantly improved the efficiency of DCP. [PERSON] [26] proposed a novel methodology based on depth approximations through DCP, local Shannon entropy, and fast guided filter to reduce artifacts and improve image recovery on sky regions with a significant decrease in calculation time. [PERSON] [27] used the depth-dependent color variation, scene ambient light difference, and adaptive color-corrected image formation model (IFM) to better restore degraded images. This method produces satisfying restored and enhanced results. Therefore, the approach has been approved to unify and generalize a wide variety of all DCP-based methods for underwater, nighttime, haze, and sandstorm images. [PERSON] [28] proposed a new haze removal technique according to DCP, which integrates the dark channel prior with CLAHE to remove the haze from color images. A bilateral filter was used to reduce the noise in images, and it showed quite effective results in noise reduction and correcting uneven illumination. In this paper, in order to overcome the problems of deep, dark pictures and incomplete defogging by the inaccurate estimation of atmospheric transmittance, the hybrid dark channel prior (HDCP) is proposed. In HDCP, Retinex [29] is first utilized to remove the interference of the illumination component and improve the brightness of the image. The atmospheric light intensity is further refined iteratively. Then, a tolerance-improved DCP is introduced to obtain the dehazed image. In the algorithm, a variant genetic algorithm (VGA) [30] is proposed to enhance the grayscale of the original image, which is used as a guided filtering image to optimize the transmittance. In order to verify the algorithm, the public datasets of O-HAZE [31] and NYU2 [32] are used as the experimental images. Compared with other DCP-based algorithms, the average _MSE_ by the proposed method decreases by 26.98%. The average _SSIM_ increases by 10.298%. The average entropy increases by 7.780%. Compared with the conventional DCP, the result of the proposed algorithm has higher brightness and a more complete degree of fog removal. There are no serious image or color distortions.