无需配对数据，用查表和扩散模型实现高效低光图像增强
Unsupervised Low-light Image Enhancement with Lookup Tables and Diffusion Priors
- 设计双查表结构：亮度调节表与噪声抑制表协同工作
- 在无监督条件下实现视觉质量超越当前最佳方法
- 适合资源受限场景下的实时低光图像修复
低光图像增强（LIE）旨在精确高效地恢复光照不足环境下的退化图像。现有先进方法多依赖深度神经网络，需大量低-正常光图像配对数据、大量参数及计算资源，实用性受限。本文提出一种基于扩散先验与查表的新型无监督LIE框架（DPLUT），包含两个关键组件：亮度调节查表（LLUT）通过无监督损失优化，预测特定图像的像素级曲线参数以动态调整动态范围；噪声抑制查表（NLUT）用于去除增亮后放大的噪声。由于扩散模型对噪声敏感，引入扩散先验以实现高性能降噪。大量实验表明，该方法在视觉质量与效率上均优于现有最先进方法。
原文摘要 · Abstract (English)
Low-light image enhancement (LIE) aims at precisely and efficiently recovering an image degraded in poor illumination environments. Recent advanced LIE techniques are using deep neural networks, which require lots of low-normal light image pairs, network parameters, and computational resources. As a result, their practicality is limited. In this work, we devise a novel unsupervised LIE framework based on diffusion priors and lookup tables (DPLUT) to achieve efficient low-light image recovery. The proposed approach comprises two critical components: a light adjustment lookup table (LLUT) and a noise suppression lookup table (NLUT). LLUT is optimized with a set of unsupervised losses. It aims at predicting pixel-wise curve parameters for the dynamic range adjustment of a specific image. NLUT is designed to remove the amplified noise after the light brightens. As diffusion models are sensitive to noise, diffusion priors are introduced to achieve high-performance noise suppression. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in terms of visual quality and efficiency.
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