基于多尺度深度曲线估计的微光图像增强算法

Low-Light Image Enhancement Algorithm Based on Multiscale Depth Curve Estimation

针对微光图像增强算法存在泛化能力较差的问题,提出一种基于多尺度深度曲线估计的微光图像增强算法,通过学习不同尺度微光图像与正常图像之间的映射关系实现微光图像增强。参数估计网络包括3个尺度的编码器和1个融合模块,可以高效直接地学习微光图像。每个编码器由级联的卷积层和池化层组成,具有特征层重复使用的优点,提高了计算效率。为增强对图像亮度的约束,提出一种亮通道损失函数。基于LIME数据集、LOL数据集和DICM数据集,对所提方法与其他6种先进算法进行对比。实验结果表明,所提方法能够得到色彩鲜艳、亮度适中、细节丰富的增强图像,在主观视觉效果和客观定量评价上均优于其他算法。

In this study,a low-light image enhancement algorithm based on multiscale depth curve estimation is proposed to address the poor generalization ability of existing algorithms.Low-light image enhancement is achieved by learning the mapping relationship between normal images and low-light images with different scales.The parameter estimation network comprises three encoders with different scales and a fusion module,facilitating the efficient and direct learning for low-light images.Furthermore,each encoder comprises cascaded convolutional and pooling layers,thereby facilitating the reuse of feature layers and improving computational efficiency.To enhance the constraint on image brightness,a bright channel loss function is proposed.The proposed method is validated against six state-of-the-art algorithms on the LIME,LOL,and DICM datasets.Experimental results show that enhanced images with vibrant colors,moderate brightness,and significant details can be obtained using the proposed method,outperforming other conventional algorithms in subjective visual effects and objective quantitative evaluations.