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Image dehazing is frequently used as a preprocessing step to enhance image clarity in various computer vision tasks, such as object tracking, intelligent surveillance and face recognition. It helps in the restoration of hazy images. Deep learning techniques have recently been utilized to significantly improve the quality of visual representation of images, but it takes a lengthy time to compute. In order to avoid the phenomenon of latency in the primary image analysis tasks, we have to monitor the time required for processing. An end-to-end modeling for real-time image dehazing network is proposed in this research paper. A complete network processing model for dehazing in real-time is proposed in this research paper. To record dependencies with reference to the positions and channels of the feature map, a non-local slice module is created. A region proposal network is also used to provide candidates with great …