提出光照感知镜像损失，提升低光图像增强自编码器的重建质量。
IAML: Illumination-Aware Mirror Loss for Progressive Learning in Low-Light Image Enhancement Auto-encoders
- 采用师生架构，逐层镜像蒸馏干净图像多尺度特征。
- 在三个数据集上实现SSIM、PSNR和LPIPS指标领先。
- 适合关注低光图像恢复与特征对齐的研究者。
本文提出一种新型训练方法与损失函数，用于低光图像增强自编码器的学习。方法基于教师-学生自编码器框架，结合渐进式学习策略，将干净图像解码器的多尺度特征以镜像方式逐层蒸馏至学生解码器各层，使用新提出的光照感知镜像损失（IAML）。IAML在对齐学生网络特征图与教师侧干净特征图时，考虑了输入图像中光照变化的影响。在三个主流低光图像增强数据集上的广泛基准测试表明，该模型在平均SSIM、PSNR和LPIPS重建精度指标上均达到当前最优性能。消融实验进一步验证了IAML对图像重建精度的显著提升作用。
原文摘要 · Abstract (English)
This letter presents a novel training approach and loss function for learning low-light image enhancement auto-encoders. Our approach revolves around the use of a teacher-student auto-encoder setup coupled to a progressive learning approach where multi-scale information from clean image decoder feature maps is distilled into each layer of the student decoder in a mirrored fashion using a newly-proposed loss function termed Illumination-Aware Mirror Loss (IAML). IAML helps aligning the feature maps within the student decoder network with clean feature maps originating from the teacher side while taking into account the effect of lighting variations within the input images. Extensive benchmarking of our proposed approach on three popular low-light image enhancement datasets demonstrate that our model achieves state-of-the-art performance in terms of average SSIM, PSNR and LPIPS reconstruction accuracy metrics. Finally, ablation studies are performed to clearly demonstrate the effect of IAML on the image reconstruction accuracy.
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