Abstract
High-quality underwater images are essential for both machine vision tasks and viewers with their aesthetic this http URL, the quality of underwater images is severely affected by light absorption and scattering. Deep learning-based methods for Underwater Image Enhancement (UIE) have achieved good performance. However, these methods often overlook considering human perception and lack sufficient constraints within the solution space. Consequently, the enhanced images often suffer from diminished perceptual quality or poor content this http URL address these issues, we propose a UIE method with a Contrastive Language-Image Pre-Training (CLIP) perception loss module and curriculum contrastive regularization. Above all, to develop a perception model for underwater images that more aligns with human visual perception, the visual semantic feature extraction capability of the CLIP model is leveraged to learn an appropriate prompt pair to map and evaluate the quality of underwater images. This CLIP perception model is then incorporated as a perception loss module into the enhancement network to improve the perceptual quality of enhanced images. Furthermore, the CLIP perception model is integrated with the curriculum contrastive regularization to enhance the constraints imposed on the enhanced images within the CLIP perceptual space, mitigating the risk of both under-enhancement and over-enhancement. Specifically, the CLIP perception model is employed to assess and categorize the learning difficulty level of negatives in the regularization process, ensuring comprehensive and nuanced utilization of distorted images and negatives with varied quality levels. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in terms of visual quality and generalization ability.
Abstract (translated)
高质量的水下图像对于机器视觉任务和观众审美需求都至关重要。然而，由于光吸收和散射的影响，水下图像的质量严重受损。基于深度学习的方法在水下图像增强（UIE）方面已经取得了很好的效果，但这些方法往往忽略了人类感知，并且解决方案空间中的约束不足。因此，经过增强的图像通常会表现出感知质量下降或内容不理想的问题。 为了解决这些问题，我们提出了一种使用对比语言-图像预训练（CLIP）感知损失模块和课程对比正则化的UIE方法。首先，为了开发一个更符合人类视觉感知的水下图像感知模型，利用了CLIP模型的视觉语义特征提取能力来学习适当的提示对，以映射并评估水下图像的质量。然后将该CLIP感知模型作为感知损失模块集成到增强网络中，从而提高增强图像的感知质量。 此外，还将CLIP感知模型与课程对比正则化相结合，在CLIP感知空间内增强了对增强图像施加的约束，减少了增强不足和过度增强的风险。具体而言，使用CLIP感知模型来评估和分类在正则化过程中负样本的学习难度水平，确保了扭曲图像和不同质量级别的负样本的全面而细微地利用。 大量的实验表明，我们的方法在视觉质量和泛化能力方面都优于现有的最先进方法。
URL
https://arxiv.org/abs/2507.06234
