Saliency detection methods are central to several real-world applications such as robot navigation and satellite imagery. However, the performance of existing methods deteriorate under low-light conditions because training datasets mostly comprise of well-lit images. One possible solution is to collect a new dataset for low-light conditions. This involves pixel-level annotations, which is not only tedious and time-consuming but also infeasible if a huge training corpus is required. We propose a technique that performs classical band-pass filtering in the Fourier space to transform well-lit images to low-light images and use them as a proxy for real low-light images. Unlike popular deep learning approaches which require learning thousands of parameters and enormous amounts of training data, the proposed transformation is fast and simple and easy to extend to other tasks such as low-light depth estimation. Our experiments show that the state-of-the-art saliency detection and depth estimation networks trained on our proxy low-light images perform significantly better on real low-light images than networks trained using existing strategies.

翻译：显著性检测方法是机器人导航和卫星图像等实际应用的核心技术。然而，现有方法在低光照条件下的性能会下降，因为训练数据集大多由光照良好的图像组成。一种可能的解决方案是为低光照条件收集新数据集。这需要像素级标注，不仅繁琐耗时，而且在需要大量训练语料时不可行。我们提出了一种在傅里叶空间进行经典带通滤波的技术，将光照良好的图像转换为低光照图像，并将其作为真实低光照图像的代理。与需要学习数千参数和大量训练数据的流行深度学习方法不同，所提出的转换方法快速、简单，且易于扩展到其他任务，如低光照深度估计。我们的实验表明，使用现有策略训练的先进显著性检测和深度估计网络，在我们代理低光照图像上训练后，在真实低光照图像上的表现显著优于现有策略训练的网络。