Abstract
Real-time low-light image enhancement on mobile and embedded devices requires models that balance visual quality and computational efficiency. Existing deep learning methods often rely on large networks and labeled datasets, limiting their deployment on resource-constrained platforms. In this paper, we propose LiteIE, an ultra-lightweight unsupervised enhancement framework that eliminates dependence on large-scale supervision and generalizes well across diverse conditions. We design a backbone-agnostic feature extractor with only two convolutional layers to produce compact image features enhancement tensors. In addition, we develop a parameter-free Iterative Restoration Module, which reuses the extracted features to progressively recover fine details lost in earlier enhancement steps, without introducing any additional learnable parameters. We further propose an unsupervised training objective that integrates exposure control, edge-aware smoothness, and multi-scale color consistency losses. Experiments on the LOL dataset, LiteIE achieves 19.04 dB PSNR, surpassing SOTA by 1.4 dB while using only 0.07\% of its parameters. On a Snapdragon 8 Gen 3 mobile processor, LiteIE runs at 30 FPS for 4K images with just 58 parameters, enabling real-time deployment on edge devices. These results establish LiteIE as an efficient and practical solution for low-light enhancement on resource-limited platforms.
Abstract (translated)
在移动和嵌入式设备上进行实时低光图像增强需要平衡视觉质量和计算效率的模型。现有的深度学习方法通常依赖于大型网络和标注数据集，这限制了它们在资源受限平台上的部署。本文提出了LiteIE，一个超轻量级无监督增强框架，该框架消除了对大规模监督信息的依赖，并且能够在各种条件下很好地泛化。我们设计了一个与主干网络无关的功能提取器，它仅包含两个卷积层，用于生成紧凑的图像特征增强张量。此外，我们开发了一个无需参数的迭代恢复模块（Iterative Restoration Module），该模块利用提取的功能逐步恢复早期增强步骤中丢失的细节点，而不需要引入任何可学习的额外参数。我们还提出了一种无监督训练目标，集成了曝光控制、边缘感知平滑度和多尺度颜色一致性损失。 在LOL数据集上的实验表明，LiteIE实现了19.04 dB PSNR（峰值信噪比），超越了现有最佳解决方案1.4 dB，同时仅使用其参数的0.07%。在Snapdragon 8 Gen 3移动处理器上，LiteIE以每秒30帧的速度处理4K图像，且只需58个参数，这使得它能够在边缘设备上实现实时部署。这些结果使LiteIE成为低光增强在资源受限平台上的高效和实用解决方案。
URL
https://arxiv.org/abs/2507.04277
