<p>Undersea images have deteriorated quality due to light scattering and absorption. The degradation of these images creates impediments in underwater tasks like recognition and detection. To improve the quality of hazy undersea images, a novel undersea image dehazing algorithm is proposed which being nascent exploits the advantages of both dark and bright channels via a two-stage framework. The first stage is devoted to restoring the underwater image via Contrast-Coded dark channel approach, and the other one is devoted to restore the image using a Color Space Transform (CST) bright channel approach. The two stages are independently used to restore the color, sharpness, contrast, and brightness of an image by taking advantage of dual channels. Any contortions left by one stage are addressed by the other stage. Subsequently, these output images from the preceding stages are then given to the final amalgamation network that gives the reconstructed image typically by combining them via efficient weight measures. The weight measures at each pel location enhance the underwater image quality by removing the halos and improving the image details. Besides, the patch estimation is performed using contrast coding techniques. Thus, the proposed algorithm facilitates better navigation and decision-making of Unmanned Underwater Vehicles, haze-free target detection, undersea archaeology, and biology research. The proposed algorithm has been evaluated using both subjective and objective assessment which reveals that our algorithm performs better compared to other state-of-the-art algorithms. The objective quality metrics used are Entropy, Blind/Referenceless Image Spatial Quality Evaluation (BRISQUE), Natural Image Quality Evaluator (NIQE), Underwater Image Sharpness Measure (UISM), Underwater Image Quality Metrics (UIQM), and Time complexity. The proposed method proffers NIQE, UIQM, and BRISQUE scores of about 3.578, 6.373, and 27.865, respectively.</p>