This paper delves into enhancing underwater images to improve human observation of underwater scenes. To this end, a reinforcement learning-based human visual perception-driven image enhancement paradigm for underwater scenes is proposed, referred to as INSPIRATION. This paradigm is so named because it constructs rewards for guiding model training by incorporating metrics inspired by human visual perception, eliminating the need for reference images. It overcomes the limitations of deep models, which often result in poor performance in dynamic and diverse underwater scenes due to the scarcity of underwater images with enhancement references. Specifically, a residual-enhancement network, composed of residual modules and a channel-attention module, is utilized to extract features as a state. An extensible collection encompassing diverse image enhancement algorithms is utilized to provide an image enhancement algorithm as an action. A multi-non-reference human visual perception metric increment is utilized as a reward, and proximal policy optimization (PPO) is utilized to conduct reinforcement learning. This paradigm treats underwater images as sole inputs during both training and implementation, learning and organizing a sequence of image enhancement algorithms to explicitly achieve a step-wise image enhancement process, aligning the enhanced images with human visual perception. Extensive qualitative and quantitative evaluations conclusively demonstrate that our paradigm outperforms nine state-of-the-art underwater image enhancement methods in terms of visual quality and achieves better performance on five underwater image quality assessment measures across three underwater image datasets. Additionally, the potential advantages of our paradigm as a pre-processing step in other underwater computer vision applications are demonstrated. The code has been released publicly for reproducibility and evaluations at .

中文翻译：

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灵感：基于强化学习的人类视觉感知驱动的水下场景图像增强范例

本文深入研究增强水下图像以改善人类对水下场景的观察。为此，提出了一种基于强化学习的人类视觉感知驱动的水下场景图像增强范式，简称INSPIRATION。这种范式之所以如此命名，是因为它通过结合受人类视觉感知启发的指标来构建指导模型训练的奖励，从而消除了对参考图像的需求。它克服了深度模型的局限性，由于缺乏具有增强参考的水下图像，深度模型常常导致在动态和多样化的水下场景中表现不佳。具体来说，利用由残差模块和通道注意模块组成的残差增强网络来提取特征作为状态。利用包含多种图像增强算法的可扩展集合来提供图像增强算法作为动作。利用多非参考人类视觉感知度量增量作为奖励，并利用近端策略优化（PPO）进行强化学习。该范例将水下图像视为训练和实施期间的唯一输入，学习和组织一系列图像增强算法，以明确地实现逐步图像增强过程，使增强后的图像与人类视觉感知保持一致。广泛的定性和定量评估最终证明，我们的范例在视觉质量方面优于九种最先进的水下图像增强方法，并且在三个水下图像数据集的五种水下图像质量评估措施中取得了更好的性能。此外，我们的范例作为其他水下计算机视觉应用中的预处理步骤的潜在优势也得到了证明。该代码已在 上公开发布以进行再现和评估。