Abstract
In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE. PIE primarily addresses three issues: (i) To resolve the problem of existing learning-based methods often training a LLE model with strict pixel-correspondence image pairs, we eliminate the need for pixel-correspondence paired training data and instead train with unpaired images. (ii) To address the disregard for negative samples and the inadequacy of their generation in existing methods, we incorporate physics-inspired contrastive learning for LLE and design the Bag of Curves (BoC) method to generate more reasonable negative samples that closely adhere to the underlying physical imaging principle. (iii) To overcome the reliance on semantic ground truths in existing methods, we propose an unsupervised regional segmentation module, ensuring regional brightness consistency while eliminating the dependency on semantic ground truths. Overall, the proposed PIE can effectively learn from unpaired positive/negative samples and smoothly realize non-semantic regional enhancement, which is clearly different from existing LLE efforts. Besides the novel architecture of PIE, we explore the gain of PIE on downstream tasks such as semantic segmentation and face detection. Training on readily available open data and extensive experiments demonstrate that our method surpasses the state-of-the-art LLE models over six independent cross-scenes datasets. PIE runs fast with reasonable GFLOPs in test time, making it easy to use on mobile devices.
Abstract (translated)
在本文中，我们提出了一个基于物理学习的对比学习范式，称为PIE。PIE主要解决了以下三个问题：（一）为了解决现有学习方法通常在严格像素对应图像对上训练LLE模型的問題，我们消除了需要像素对应的一对训练数据，而是使用未配对的图像进行训练。 （二）为了解决现有方法忽视负样本以及它们的生成不夠合理的问题，我们引入了基于物理的对比学习LLE，并设计了Bag of Curves（BoC）方法来生成更合理的负样本，使其更贴近底层物理成像原理。 （三）为了克服现有方法在现有方法中依赖语义真实值的问题，我们提出了一个无监督的区域分割模块，在确保区域亮度一致性的同时消除对语义真实值的依赖。 总体而言，与现有的LLE方法相比，所提出的PIE具有明显的优势。除了PIE的新架构外，我们还研究了PIE在下游任务（如语义分割和面部检测）上的性能提升。在易于获取的开源数据上进行训练，并进行了广泛的实验，结果表明，我们的方法在六个独立场景数据集上的性能超越了最先进的LLE模型。PIE在测试时间具有合理的GFLOPs，使其在移动设备上使用方便。
URL
https://arxiv.org/abs/2404.04586
