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.

中文翻译：

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基于U-net的低光图像增强多尺度特征保留方法

低光图像增强是一项具有挑战性的任务，一直是一个热门的研究课题。受retinex理论和U-Net网络的启发，我们提出了一种基于U-Net的低光图像增强多尺度特征保留方法，可以实现低层特征和高层语义特征的提取。在特征提取之前，我们对图像进行多尺度预提取处理，以提高网络的特征提取能力。考虑到低级特征和高级语义特征之间的不连续性，我们提出了一种空间一致性方法来保持全局特征相关性。最后，我们提出了一种新的多尺度结构计算方法，大大缓解了增强后照度不均和颜色偏差的现象，使增强结果更符合人类视觉感知。大量实验表明，与其他先进的增强方法相比，我们的方法具有更好的增强效果，可以保留更多的细节。增强后的图像不仅具有良好的视觉感知，而且在客观评价方面也优于其他方法。