U-net-based multiscale feature preserving method for low light image enhancement

Abstract

Low light image enhancement is a challenging task, and it has been a hot research topic. Inspired by retinex theory and U-Net network, we propose a U-Net-based multiscale feature preserving method for low light image enhancement, which can realize the extraction of low-level features and high-level semantic features. Before feature extraction, we carry out multiscale pre-extraction processing on the image to improve the feature extraction ability of the network. Considering the discontinuity between low-level features and high-level semantic features, we propose a spatial consistency method to maintain the global feature correlation. Finally, we propose a new multiscale structure calculation method, which greatly alleviates the phenomenon of uneven illumination and color deviation after enhancement and makes the enhancement results more consistent with human visual perception. Extensive experiments demonstrate that compared with other advanced enhancement methods, our method has better enhancement effect and can retain more details. The enhanced image not only has good visual perception but also is better than other methods in objective evaluation.