Semi-LLIE：基于Mamba的弱光图像增强半监督对比学习
Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement
AI总结：
针对弱光图像增强中成对数据稀缺问题，提出基于均值教师的半监督框架Semi-LLIE，通过引入语义感知对比损失、基于Mamba的多尺度主干网络及RAM感知损失，有效提升色彩还原与纹理细节，在定量和定性指标上超越现有方法。
摘要：
尽管最近的弱光图像增强技术取得了显著进展，但成对数据的稀缺已成为进一步发展的重大障碍。本文提出了一种基于均值教师的半监督弱光增强（Semi-LLIE）框架，将未配对数据整合到模型训练中。均值教师技术是一种杰出的半监督学习方法，已成功用于解决高级和低级视觉任务。然而，两个主要问题阻碍了朴素的均值教师方法在弱光图像增强中达到最佳性能。首先，像素级一致性损失不足以将真实的光照分布从教师模型转移到学生模型，这会导致增强图像中出现色偏。其次，前沿的图像增强方法无法有效地与均值教师框架合作以恢复暗区中的详细信息，因为它们倾向于忽略局部区域内结构化信息的建模。为了缓解上述问题，我们首先引入了语义感知对比损失来忠实地转移光照分布，有助于增强具有自然色彩的图像。然后，我们设计了基于Mamba的弱光图像增强主干网络，通过多尺度特征学习方案有效增强Mamba的局部区域像素关系表示能力，促进生成具有丰富纹理细节的图像。此外，我们基于大规模视觉语言Recognize Anything Model（RAM）提出了新颖的感知损失，以帮助生成具有更丰富文本细节的增强图像。实验结果表明，我们的Semi-LLIE在定量和定性指标上均超越了现有方法。
英文摘要：
Despite the impressive advancements made in recent low-light image enhancement techniques, the scarcity of paired data has emerged as a significant obstacle to further advancements. This work proposes a mean-teacher-based semi-supervised low-light enhancement (Semi-LLIE) framework that integrates the unpaired data into model training. The mean-teacher technique is a prominent semi-supervised learning method, successfully adopted for addressing high-level and low-level vision tasks. However, two primary issues hinder the naive mean-teacher method from attaining optimal performance in low-light image enhancement. Firstly, pixel-wise consistency loss is insufficient for transferring realistic illumination distribution from the teacher to the student model, which results in color cast in the enhanced images. Secondly, cutting-edge image enhancement approaches fail to effectively cooperate with the mean-teacher framework to restore detailed information in dark areas due to their tendency to overlook modeling structured information within local regions. To mitigate the above issues, we first introduce a semantic-aware contrastive loss to faithfully transfer the illumination distribution, contributing to enhancing images with natural colors. Then, we design a Mamba-based low-light image enhancement backbone to effectively enhance Mamba's local region pixel relationship representation ability with a multi-scale feature learning scheme, facilitating the generation of images with rich textural details. Further, we propose novel perceptive loss based on the large-scale vision-language Recognize Anything Model (RAM) to help generate enhanced images with richer textual details. The experimental results indicate that our Semi-LLIE surpasses existing methods in both quantitative and qualitative metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
