在水下图像增强中向比伪参考更好的方向迈进
概括
这项研究引入了一种新的混合损失函数,即性能超越损失 (PSPL),以改善无需复杂网络的水下图像增强 (UIE). 即使训练数据不完美,PSPL也可以帮助生成更高质量的图像.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 现有的水下图像增强 (UIE) 方法通常依赖于带有不完美的伪参考标签的配对数据集.
- 这种局限性阻碍了当前IEU网络的性能,需要改进培训策略.
研究的目的:
- 为UIE开发先进的损失功能,克服不完善的伪参考标签造成的性能瓶.
- 在不使用更复杂的网络架构的情况下,提高UIE网络性能.
主要方法:
- 制定了一个插入式混合性能超越损失 (PSPL),结合了质量得分比较损失 (QSCL) 和深度感知无配对对比损失 (DUCL).
- QSCL通过比较图像和区域级质量分数与伪引用来指导增强.
- DUCL通过利用场景深度信息来解决严重退化的偏远地区问题,将偏远地区拉向附近的高质量地区,并将它们从低质量的偏远地区推开.
主要成果:
- 与最先进的方法相比,拟议的PSPL显著提高了UIE的性能.
- 即使在使用简单轻量级的UIE网络架构时,PSPL的有效性也得到了证明.
- 实验结果验证了拟议的损失函数在增强退化水下图像方面的优越性.
结论:
- 开发的PSPL通过专注于高级损失函数,为水下图像增强提供了一种新且有效的方法.
- 这种方法成功地突破了与UIE中不完美的培训数据相关的性能限制.
- 该PSPL框架为改善水下图像的视觉质量提供了一个强大的解决方案,即使在简单的网络设计中也适用.
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