In this paper we propose a learning-based restoration approach to learn the optimal parameters for enhancing the quality of different types of underwater images and apply a set of intensity transformation techniques to process raw underwater images. The methodology comprises two steps. Firstly, a Convolutional Neural Network (CNN) Regression model is employed to learn enhancing parameters for each underwater image type. Trained on a diverse dataset, the CNN captures complex relationships, enabling generalization to various underwater conditions. Secondly, we apply intensity transformation techniques to raw underwater images. These transformations collectively compensate for visual information loss due to underwater degradation, enhancing overall image quality. In order to evaluate the performance of our proposed approach, we conducted qualitative and quantitative experiments using well-known underwater image datasets (U45 and UIEB), and using the proposed challenging dataset composed by 276 underwater images from the Amazon region (AUID). The results demonstrate that our approach achieves an impressive accuracy rate in different underwater image datasets. For U45 and UIEB datasets, regarding PSNR and SSIM quality metrics, we achieved 26.967, 0.847, 27.299 and 0.793, respectively. Meanwhile, the best comparison techniques achieved 26.879, 0.831, 27.157 and 0.788, respectively.

在本文中，我们提出了一种基于学习的恢复方法来学习提高不同类型水下图像质量的最佳参数，并应用一组强度变换技术来处理原始水下图像。该方法包括两个步骤。首先，采用卷积神经网络（CNN）回归模型来学习每种水下图像类型的增强参数。 CNN 在不同的数据集上进行训练，可以捕获复杂的关系，从而能够泛化到各种水下条件。其次，我们将强度变换技术应用于原始水下图像。这些转换共同补偿了由于水下退化而导致的视觉信息损失，从而提高了整体图像质量。为了评估我们提出的方法的性能，我们使用著名的水下图像数据集（U45和UIEB）进行定性和定量实验，并使用由来自亚马逊地区的276张水下图像（AUID）组成的提出的具有挑战性的数据集。结果表明，我们的方法在不同的水下图像数据集中实现了令人印象深刻的准确率。对于 U45 和 UIEB 数据集，关于 PSNR 和 SSIM 质量指标，我们分别达到了 26.967、0.847、27.299 和 0.793。同时，最佳比较技术分别达到 26.879、0.831、27.157 和 0.788。