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Image dehazing is an essential preprocessing step for various computer vision applications. Haze is a common atmospheric phenomenon that reduces the visibility of images captured in outdoor environments. In recent years, deep learning-based approaches have shown promising results in image dehazing. This paper proposes a novel deep learning-based approach for single image dehazing using convolutional neural networks (CNNs). The proposed method learns to estimate the transmission map and atmospheric light simultaneously, resulting in a more accurate and efficient dehazing process. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed approach. If you have more information about where you
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