采用惯性 Bregman 交替线性化最小化的边缘引导低光照图像增强
Edge-guided Low-light Image Enhancement with Inertial Bregman Alternating Linearized Minimization
AI总结：
针对低光照图像先验提取困难，本文提出边缘提取先验与边缘引导 Retinex 模型，并用惯性 Bregman 交替线性化最小化算法求解，证明其非凸收敛性且实验验证优越。
摘要：
基于先验的低光照图像增强方法在从昏暗图像中提取可用先验信息时往往面临挑战。为克服这一局限，我们结合所提出的边缘提取先验，引入了一个简单而有效的 Retinex 模型。更具体地说，我们设计了一个边缘提取网络，用于直接从低光照图像中捕获精细边缘特征。在 Retinex 理论的基础上，我们将低光照图像分解为其光照分量和反射分量，并引入边缘引导的 Retinex 模型来增强低光照图像。为求解所提出的模型，我们提出了一种新的惯性 Bregman 交替线性化最小化算法。该算法处理与边缘引导 Retinex 模型相关的优化问题，从而能够有效增强低光照图像。通过严格的理论分析，我们建立了该算法的收敛性质。此外，我们通过非凸优化理论证明了所提算法会收敛到该问题的稳定点。进一步地，我们在多个真实世界低光照图像数据集上开展了大量实验，以证明所提方案的效率和优越性。
英文摘要：
Prior-based methods for low-light image enhancement often face challenges in extracting available prior information from dim images. To overcome this limitation, we introduce a simple yet effective Retinex model with the proposed edge extraction prior. More specifically, we design an edge extraction network to capture the fine edge features from the low-light image directly. Building upon the Retinex theory, we decompose the low-light image into its illumination and reflectance components and introduce an edge-guided Retinex model for enhancing low-light images. To solve the proposed model, we propose a novel inertial Bregman alternating linearized minimization algorithm. This algorithm addresses the optimization problem associated with the edge-guided Retinex model, enabling effective enhancement of low-light images. Through rigorous theoretical analysis, we establish the convergence properties of the algorithm. Besides, we prove that the proposed algorithm converges to a stationary point of the problem through nonconvex optimization theory. Furthermore, extensive experiments are conducted on multiple real-world low-light image datasets to demonstrate the efficiency and superiority of the proposed scheme.
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