Abstract
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.
Abstract (translated)
低光图像增强（LIE）旨在精确有效地恢复在欠光照环境下受损的图像。最近，先进的LIE技术采用深度神经网络，这需要大量的低正常光图像对、网络参数和计算资源。因此，它们的实用性受到限制。在本文中，我们提出了一种基于扩散优先权和查找表（DPLUT）的新型无监督LIE框架，以实现高效的低光图像恢复。所提出的方法包括两个关键组件：一个光调整查找表（LLUT）和一个噪声抑制查找表（NLUT）。LLUT通过一系列无监督损失进行优化。它旨在预测特定图像的动态范围调整过程中的像素级曲线参数。NLUT在光变亮后设计用于消除放大噪声。由于扩散模型对噪声敏感，我们引入了扩散优先权来实现高性能的噪声抑制。大量实验证明，我们的方法在视觉质量和效率方面超过了最先进的方法。
URL
https://arxiv.org/abs/2409.18899
