Recent approaches for low-light image enhancement achieve excellent performance through supervised learning based on convolutional neural networks. However, it is still challenging to collect a large amount of low-/normal-light image pairs in real environments for training the networks. In this letter, we propose an unsupervised learning approach for single low-light image enhancement using the bright channel prior (BCP) that the brightest pixel in a small patch is likely to be close to 1. An unsupervised loss function is defined with the pseudo ground-truth generated using the BCP. An enhancement network, consisting of a simple encoder-decoder, is then trained using the unsupervised loss function. To the best of our knowledge, this is the first attempt that enhances a low-light image through unsupervised learning. Furthermore, we introduce saturation loss and self-attention map for preserving image details and naturalness in the enhanced result. The performance of the proposed method is validated on various public datasets. Experimental results demonstrate that the proposed unsupervised approach achieves competitive performance over state-of-the-art methods based on supervised learning.

中文翻译：

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使用明亮通道先验的无监督低光图像增强

最近的低光图像增强方法通过基于卷积神经网络的监督学习实现了出色的性能。然而，在真实环境中收集大量低光/正常光图像对来训练网络仍然具有挑战性。在这封信中，我们提出了一种使用明亮通道先验 (BCP) 进行单个低光图像增强的无监督学习方法，即小块中最亮的像素可能接近 1。无监督损失函数定义为伪使用 BCP 生成的地面实况。然后使用无监督损失函数训练一个由简单的编码器-解码器组成的增强网络。据我们所知，这是通过无监督学习增强低光图像的第一次尝试。此外，我们引入了饱和度损失和自注意力图，以在增强结果中保留图像细节和自然度。所提出方法的性能在各种公共数据集上得到验证。实验结果表明，与基于监督学习的最新方法相比，所提出的无监督方法取得了有竞争力的性能。