###### Abstract  
Image dehazing plays a pivotal role in numerous computer vision applications such as object recognition, surveillance systems, and security systems, where it can be considered as an introductory stage. Recently, many proposed learning-based works address this significant task; however, most of them neglect the atmospheric light estimation and fail to produce accurate transmission maps. To address such a problem, in this paper, we propose a two-stage dehazing system. The first stage presents an accurate atmospheric light algorithm labeled \"A-Est\" that employs hazy image blurriness and quadtree decomposition. Te second stage represents a cascaded multi-scale CNN model called CMTnet that consists of two subnetworks, one for calculating rough transmission maps (CMCNNtr) and the other for its refinement (CMCNNtr). Each subnetwork is composed of three-layer D-units (D indicates dense). Experimental analysis and comparisons with state-of-the-art dehazing methods revealed that the proposed system can estimate \(AL\) and \(t\) efficiently and accurately by achieving high-quality dehazing results and outperforms state-of-the-art comparative methods according to SSIM and MSE values, where our proposed achieves the best scores of both (91% average SSIM and 0.068 average MSE). Keywords:Image dehazing; atmospheric light; transmission map; Convolutional Neural Networks (CNN); Multi-scale Convolutional Neural Networks (MCNN) +
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the visual aspect of the hazy image in the case of bad lighting conditions. All these aforementioned dehazing methods rely on image quality improvements, such as contrast enhancement and edges visibility and ignore the real mechanism of degradation. Despite the simplicity and enhancement attained by these techniques, however, some haze particles still appear on the recovered hazy images. These methods are called image enhancement-based dehazing. Simultaneously, polarization-based techniques [6; 7; 8] were introduced to recover the scene radiance of hazy images using multiple image degrees. In addition, under different atmospheric conditions of hazy images of the same scenery, the authors of [9; 10] proposed a new way of dehazing method. These dehazing methods perform well and recover free-haze images. However, their common problem is that additional information is required, whether an image or another kind of information, which is hard to provide. This limit makes the dehazing operation more difficult and sometimes impossible. Recently, the advent of the widely used physical hazy image degradation model [11] has incited numerous researchers to use it for image dehazing task. Thus, Markov Random Field (MRF)-based physical model has been exploited to dehaze single images. This idea is derived from the assumption that the local contrast of haze-free images is much higher than that of hazy images. In [12], a local contrast maximization method was proposed to restore images, but this method generates over-saturated recovered images. [PERSON] et al. [13], in turn, took advantage of the Independent Component Analysis (ICA) method to separate the haze layer from scene objects and then recover hazy images. However, this method fails to recover hazy images with a dense haze and consumes a superior time complexity. To address this significant problem, [PERSON] et al. [14] proposed a dehazing based prior method (dark channel prior (DCP)) to estimate the thickness of haze, which stands on the physical degradation model. This method is a robust, recently discovered approach in image dehazing research area under the assumption of dark pixels. Despite the success achieved by the DCP approach, it cannot remove haze from the sky region in hazy images and it has poor edge consistency. Many attempts have been proposed to overcome these deficiencies. In [15], [PERSON] et al. also used the basic structure of DCP approach to build an effective method for real-time image and video dehazing.