基于U-Net的多尺度低照度图像增强网络

Multi-Scale Low-Light Image Enhancement Network Based on U-Net

低照度是夜晚拍摄时常见的一种现象,不充分的光照会使图像细节损失严重,降低图像视觉质量。针对现有低照度图像增强方法对不同尺度特征的感知和表达能力存在不足的问题,提出一种基于U-Net的多尺度低照度图像增强网络(MSU-LIIEN)。采用特征金字塔作为基本处理框架,实现对低照度图像的特征提取。在特征金字塔构建的3个分支结构中均使用U-Net作为骨干网,对提取到的浅层图像特征进行编码与解码操作,同时引入结构细节残差融合块以增强网络模型提取和表征低照度图像特征信息的能力。在此基础上,对提取到的特征信息逐层融合,恢复正常光照图像。实验结果表明,MSU-LIIEN在LOL-datasets和Brighting Train数据集中相比于性能排名第二的KinD模型,平均峰值信噪比分别提高16.21%和46.67%,且在主观视野感受和客观评价指标方面均优于所有对比的经典模型,不但能有效提升低照度图像的整体亮度,而且能很好地保持图像中的细节信息和清晰的物体边缘轮廓,使增强后的图像整体画面真实自然。

Low light is a common phenomenon when shooting at night.Insufficient illumination causes serious loss of image details and reduces visual quality.The existing low-light image enhancement methods have insufficient perception and expression of features at different scales.To address the problem of existing low-light image enhancement methods being inadequate in their ability to perceive and express features at different scales,a multi-scale low-light image enhancement network based on U-Net(MSU-LIIEN)is proposed.Firstly,the feature pyramid is used as the basic processing framework of this article to achieve feature extraction for low-light images.Then,the U-Net is used as the backbone in all three branch structures of the feature pyramid construction to encode and decode the extracted shallow image features,while structural detail residual fusion blocks are introduced to enhance the network’s ability to extract and characterize low-light image feature information.Finally,the extracted feature information is fused layer by layer to recover the final image.The experimental results show that,compared with the second-performing KinD algorithm in LOL-datasets,the average Peak Signal-to-Noise Ratio(PSNR)value increased by 16.21%,and compared with the second-performing model on the Brighting Train dataset,the average PSNR value increased by 49.67%.The proposed algorithm outperforms other classical low-light image enhancement algorithms in terms of both subjective visual field perception and objective evaluation metrics.Not only does it effectively enhance the overall brightness of low-light images,but it also maintains detailed information in the image and clear object outlines,making the overall picture of the enhanced image realistic and natural.