Spectrum-inspired Low-light Image Translation for Saliency Detection
This addresses the challenge of collecting annotated low-light datasets for computer vision tasks, offering a fast and simple alternative to deep learning methods.
The paper tackles the problem of saliency detection performance deteriorating under low-light conditions by proposing a Fourier-based band-pass filtering technique to transform well-lit images into proxy low-light images for training, resulting in significantly better performance on real low-light images for saliency detection and depth estimation networks compared to existing strategies.
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.