###### Abstract
They used the guided filter as a transmission map refinement step, and the maximum of DCP as atmospheric light value. [PERSON] et al. [16] benefited from the dark channel prior and combined it with the fast bilateral filter to design their dehazing method. In addition, [PERSON] et al. [17], [PERSON] et al. [18], and [PERSON] et al. [19] adopted the original DCP in their proposed dehazing methods. However, despite the effectiveness and simplicity of these follow-up methods, the results still have the problem of over-enhancement due to the atmospheric light inaccuracy estimation, and halo effects on the edges. [PERSON] et al. [20] proposed a simple dehazing method based on a linear model to recover the scene depth. However, this method is not efficient enough, particularly for images with thick haze. At present, Convolutional Neural Networks (CNNs) have attained great success in addressing several low-level computer vision and image processing tasks, e.g., image segmentation [21; 22; 23], object detection [24], and image denoising [25]. Many researchers [26; 27; 28; 29] have applied Convolutional Neural Networks (CNNs) to explore haze-relevant features deeply. [PERSON] et al. and [PERSON] et al. [26; 27] proposed multi-scale CNN frameworks to calculate the transmission medium from the input hazy image directly. [PERSON] et al. [28] presented an End-to-End framework based on CNNs estimating the transmission of image pixels within small patches. To make the learning process easier, [PERSON] et al. [29] added a new ranking layer into the classical CNN framework, which can capture the structural and statistical attributes jointly. Despite the impressive achievement of learning-based dehazing techniques, they still have some problems that appear in results, in terms of saturation, and naturalness of recovered hazy image because of non-massive data in the learning operation. In addition, redundant computations increase the computational complexity, as mentioned in [PERSON] et al. 's [29] conclusion. Additionally, most of the existing CNN-based dehazing approaches estimate only the transmission medium of hazy images and neglect the atmospheric light estimation step. This shows the inadequate representation of the widely used hazy image degradation model (Equation (1)) because it still requires the estimation of another important parameter, namely the atmospheric light \(A\), which is absent in their proposed dehazing models. To tackle the inherent limitations mentioned above, in this paper, we propose a new efficient and robust dehazing system that comprises two main stages: the efficient atmospheric light (\(AL\)) estimation algorithm \"A-Est\" and the cascaded CMTnet-based transmission map estimator that has two subnetworks. The first estimates the rough transmission medium of the hazy image and the other refines the estimated transmission map. These two subnetworks are generated jointly within the proposed CMTnet network. The main contributions presented in our work can be summarized as follows. First, we propose a new accurate atmospheric light estimation algorithm \"A-Est\" that avoids the problem of over-saturation posed by most of the proposed dehazing approaches. Then, we build a two-task cascaded MCNN-based transmission map estimator, which directly generates the rough transmission medium of input hazy image through the CMCNN\({}_{tr}\), and then refines it by the second subnetwork (CMCNN\({}_{t}\)), inspired by [PERSON] (2018) and [PERSON] (2018).