论文标题
通过区域感知归一化的局部低光图像增强
Local Low-light Image Enhancement via Region-Aware Normalization
论文作者
论文摘要
在弱光图像增强（LLIE）领域中，现有的研究主要集中在全球增强图像上。但是，许多应用程序都需要本地LLIE，其中允许用户使用输入掩码来照亮特定区域，例如创建主角阶段或聚光灯效应。但是，此任务目前受到了有限的关注。本文旨在系统地定义本地LLIE的要求，并提出一种新颖的策略，以将当前现有的全球LLIE方法转换为本地版本。图像空间分为三个区域：蒙面区域启发以达到所需的照明效果；过渡区B是从开明区域（A区）到未改变区域（C区）的平稳过渡。为了实现本地llie的任务，我们引入了局部增强的区域范围，以兰伦为名。 Ranlen使用动态设计的基于掩码的标准化操作，该操作以空间变化的方式增强图像，以确保增强结果与输入蒙版指定的要求一致。此外，制定了一组地区感知的损失条款，以促进学习当地的LLIE框架。我们的策略可以应用于具有不同结构的现有全球LLIE网络。广泛的实验表明，与全球LLIE相比，我们的方法可以产生所需的照明效果，同时，通过各种掩模形状提供可控制的局部增强功能。
In the realm of Low-Light Image Enhancement (LLIE), existing research primarily focuses on enhancing images globally. However, many applications require local LLIE, where users are allowed to illuminate specific regions using an input mask, such as creating a protagonist stage or spotlight effect. However, this task has received limited attention currently. This paper aims to systematically define the requirements of local LLIE and proposes a novel strategy to convert current existing global LLIE methods into local versions. The image space is divided into three regions: Masked Area A be enlightened to achieve the desired lighting effects; Transition Area B is a smooth transition from the enlightened area (Area A) to the unchanged region (Area C). To achieve the task of local LLIE, we introduce Region-Aware Normalization for Local Enhancement, dubbed as RANLEN. RANLEN uses a dynamically designed mask-based normalization operation, which enhances an image in a spatially varying manner, ensuring that the enhancement results are consistent with the requirements specified by the input mask. Additionally, a set of region-aware loss terms is formulated to facilitate the learning of the local LLIE framework. Our strategy can be applied to existing global LLIE networks with varying structures. Extensive experiments demonstrate that our approach can produce the desired lighting effects compared to global LLIE, all the while offering controllable local enhancement with various mask shapes.
