A cutting-edge ensemble model for enhanced underwater image restoration and quality improvement

Abstract Underwater image enhancement poses unique challenges due to poor visibility, color distortion, and haze caused by light absorption and scattering in water. In this paper, we propose an ensemble model, Ensemble Pyramid-based Convolutional Neural Network and Deep Channel Prior Dehazing Network (EPCNN-DCPDN), which combines Pyramid-based Convolutional Neural Networks (CNNs) and the Deep Channel Prior Dehazing Network (DCPDN) to address these challenges. The model operates in two ways: sequentially, by first applying DCPDN for haze removal followed by Pyramid-based CNNs for multi-scale feature refinement, or in parallel, with outputs from both models fused using a weighted average or learned fusion mechanism. We evaluated the proposed model on multiple underwater datasets and compared its performance against nine state-of-the-art models, including CLAHE, FUnIE-GAN, WaterGAN, and Haze-Line Prior Model. The EPCNN-DCPDN model achieved superior results with a PSNR of 28.34 dB, SSIM of 0.902, and UIQM of 3.56. It also demonstrated outstanding accuracy in challenging underwater conditions, with an accuracy of 97.92% on shallow, deep, and low-light underwater datasets, outperforming existing models such as WaterGAN and Haze-Line Prior Model. The results highlight the effectiveness of the proposed model in restoring color, contrast, and fine details in underwater images. The model’s ability to handle a wide range of underwater conditions makes it an ideal solution for applications in underwater exploration, marine research, and object detection.