In this paper, a two-step image enhancement is presented. In the first step, color correction and underwater image quality enhancement are conducted if there are artifacts such as darkening, hazing and fogging. In the second step, the image resolution optimized in the previous step is enhanced using the convolutional neural network (CNN) with deep learning capability. The main reason behind the adoption of this two-step technique, which includes image quality enhancement and super-resolution, is the need for a robust strategy to visually improve underwater images at different depths and under diverse artifact conditions. The effectiveness and robustness of the real-time algorithm are satisfactory for various underwater images under different conditions, and several experiments have been undertaken for the two datasets of images. In both stages and for each of image datasets, the mean square error (MSE), peak signal to noise ratio (PSNR), and structural similarity (SSIM) evaluation measures were fulfilled. In addition, the low computational complexity and suitable outputs were obtained for different artifacts that represented divergent depths of water to achieve a real-time system. The super-resolution in the proposed structure for medium layers can offer a proper response. For this reason, time is also one of the major factors reported in the research. Applying this model to underwater imagery systems will yield more accurate and detailed information.

在本文中，提出了两步图像增强。第一步，如果存在诸如暗化，雾化和雾化等伪影，则进行色彩校正和水下图像质量增强。在第二步中，使用具有深度学习功能的卷积神经网络（CNN）增强了在上一步中优化的图像分辨率。采用包括图像质量增强和超分辨率在内的两步技术背后的主要原因是，需要一种鲁棒的策略来在视觉上改善不同深度和不同伪影条件下的水下图像。对于不同条件下的各种水下图像，实时算法的有效性和鲁棒性令人满意，并且已经针对这两个图像数据集进行了一些实验。在两个阶段以及每个图像数据集，均方均误差（MSE），峰值信噪比（PSNR）和结构相似度（SSIM）评估指标均得到满足。另外，对于代表水的不同深度的不同伪像获得了低计算复杂度和合适的输出，以实现实时系统。所提出的用于中间层的结构中的超分辨率可以提供适当的响应。因此，时间也是该研究报告的主要因素之一。将此模型应用于水下影像系统将产生更准确和详细的信息。对于代表水深不同的不同工件，可以获得较低的计算复杂度和合适的输出，以实现实时系统。所提出的用于中间层的结构中的超分辨率可以提供适当的响应。因此，时间也是该研究报告的主要因素之一。将此模型应用于水下影像系统将产生更准确和详细的信息。对于代表水深不同的不同工件，可以获得较低的计算复杂度和合适的输出，以实现实时系统。所提出的用于中间层的结构中的超分辨率可以提供适当的响应。因此，时间也是该研究报告的主要因素之一。将此模型应用于水下影像系统将产生更准确和详细的信息。