论文标题
PSENET：无监督极限图像增强的渐进自我增强网络
PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement
论文作者
论文摘要
照明的极端（例如，光线过多）通常会给机器和人类视力带来许多麻烦。许多最近的作品主要集中在暴露不足的情况下，在暴露情况下，通常在低光条件下（例如夜间）捕获图像，并取得了令人鼓舞的结果以提高图像的质量。但是，它们不如在过度曝光下处理图像。为了减轻这种限制，我们提出了一种新型的无监督增强框架，该框架在各种照明条件下具有鲁棒性，而不需要任何暴露良好的图像作为地面真相。我们的主要概念是构建从多个源图像合成的伪地面图像，这些图像模拟了所有潜在的曝光场景以训练增强网络。我们的广泛实验表明，在定量指标和定性结果方面，所提出的方法始终优于几个公共数据集中当前无监督的对应物。我们的代码可在https://github.com/vinairesearch/psenet-image-enhancement上找到。
The extremes of lighting (e.g. too much or too little light) usually cause many troubles for machine and human vision. Many recent works have mainly focused on under-exposure cases where images are often captured in low-light conditions (e.g. nighttime) and achieved promising results for enhancing the quality of images. However, they are inferior to handling images under over-exposure. To mitigate this limitation, we propose a novel unsupervised enhancement framework which is robust against various lighting conditions while does not require any well-exposed images to serve as the ground-truths. Our main concept is to construct pseudo-ground-truth images synthesized from multiple source images that simulate all potential exposure scenarios to train the enhancement network. Our extensive experiments show that the proposed approach consistently outperforms the current state-of-the-art unsupervised counterparts in several public datasets in terms of both quantitative metrics and qualitative results. Our code is available at https://github.com/VinAIResearch/PSENet-Image-Enhancement.
