Abstract Low-light image enhancement has been very popular for improving the visual quality of image representations, while low-light images often require advanced techniques to improve the perception of information for a human viewer. One of the main objectives in increasing the lighting conditions is to retain patterns, texture, and style with minimal deviations from the considered image. To this direction, we propose a low-light image enhancement method with Haar wavelet-based pooling to preserve texture regions and increase their quality Low-light image enhancement based on U-Net and Haar wavelet Elissavet Batziou1,2, Konstantinos Ioannidis1, Ioannis Patras2, Stefanos Vrochidis1, Ioannis Kompatsiaris1 1Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece {batziou.el,kioannid,stefanos,ikom}@iti.gr 2School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road London E1 4NS {e.batziou,ipatras}@qmul.ac.uk Introduction Image acquisition in low-lighting conditions is challenging to be improved when low brightness, low contrast, a narrow gray range, color distortion and severe noise exists, and no other sensor is involved as for example multiple cameras of different specifications. Challenges: • maximize the information involved from the input image on the available patterns • extract the colour information that is hidden behind the low values of luminance. Contribution of our work: • Photorealistic style transfer for under-exposed image enhancement. Feature aggregation combines features of different scales to reach an improved image reconstruction through photorealistic style transfer to achieve a natural stylization effect. • A modified U-Net based architecture is proposed that involves dense blocks and wavelet pooling layers. • Encoding using low frequency (LL) component and unpooling using high frequency components (LH, HL, HH). Haar wavelet decomposes the original signal into channels that capture different components, which leads to better image enhancement. Methodology A modified U-Net-based network is combined with wavelet transformations and Adaptive Instance Normalization (AdaIN). The proposed network is comprised by the following three sub-components: encoder, enhancer and decoder. Results (cont.) Conclusion and Future work • A photorealistic style transfer approach is adapted and modified to low-light image enhancement. • The method is based on a U-net architecture using dense blocks and three main modules, namely encoder, enhancer and decoder. • The encoder and enhancer include a Haar wavelet based pooling layer. • Experiments in two datasets compared the proposed method with recent works in image enhancement, showing that the proposed approach achieves SoA with less computational resources in training and testing processes. • Future modifications will focus on evaluating further the impact of the wavelet incorporation and their deployment in other levels of the architecture. Acknowledgements This work was partially supported by the European Commission under contracts H2020-952133 xR4DRAMA and H2020-958161 ASHVIN. Decoder: mirrors the encoder structure (yellow box) with four upsampling blocks. Each block consists of: • an upsampling layer • three convolutional layers • a concatenation operation that receives the corresponding encoder feature coming from the enhancer module. Enhancer: passes multi-scale features from the encoder to the decoder (purple box). Feature aggregation allows the network to incorporate spatial information from various scales and keep the detailed information of the input image. Encoder: has convolutional layers and downsampling dense blocks (blue box). Downsampling dense blocks contain: • a Haar wavelet-based pooling layer • a dense block (three densely connected conv. layers) • two convolutional layers Datasets LOL dataset has 500 image pairs: • 485 pairs randomly selected for training • 15 pairs for testi