No Fear of the Dark: Image Retrieval under Varying Illumination Conditions
This addresses the problem of robust image retrieval for applications like surveillance or autonomous systems in changing lighting, but it is incremental as it builds on existing normalization techniques.
The paper tackles image retrieval under varying illumination conditions, such as between day and night, by proposing a learnable photometric normalization method based on U-Net, which outperforms standard approaches in these conditions while matching state-of-the-art on daylight benchmarks.
Image retrieval under varying illumination conditions, such as day and night images, is addressed by image preprocessing, both hand-crafted and learned. Prior to extracting image descriptors by a convolutional neural network, images are photometrically normalised in order to reduce the descriptor sensitivity to illumination changes. We propose a learnable normalisation based on the U-Net architecture, which is trained on a combination of single-camera multi-exposure images and a newly constructed collection of similar views of landmarks during day and night. We experimentally show that both hand-crafted normalisation based on local histogram equalisation and the learnable normalisation outperform standard approaches in varying illumination conditions, while staying on par with the state-of-the-art methods on daylight illumination benchmarks, such as Oxford or Paris datasets.