Images captured under real-world low-light conditions face significant challenges due to uneven ambient lighting, making it difficult for existing end-to-end methods to enhance images with a large dynamic range to normal exposure levels. To address the above issue, we propose a novel brightness-adaptive enhancement framework designed to tackle the challenge of local exposure inconsistencies in real-world low-light images. Specifically, our proposed framework comprises two components: the Local Contrast Enhancement Network (LCEN) and the Global Illumination Guidance Network (GIGN). We introduce an early stopping mechanism in the LCEN and design a local discriminative module, which adaptively perceives the contrast of different areas in the image to control the premature termination of the enhancement process for patches with varying exposure levels. Additionally, within the GIGN, we design a global attention guidance module that effectively models global illumination by capturing long-range dependencies and contextual information within the image, which guides the local contrast enhancement network to significantly improve brightness across different regions. Finally, in order to coordinate the LCEN and GIGN, we design a novel training strategy to facilitate the training process. Experiments on multiple datasets demonstrate that our method achieves superior quantitative and qualitative results compared to state-of-the-art algorithms.

翻译：在真实世界低照度条件下拍摄的图像，由于环境光照不均匀而面临重大挑战，使得现有端到端方法难以将具有大动态范围的图像增强至正常曝光水平。为解决上述问题，我们提出一种新颖的亮度自适应增强框架，旨在应对真实低照度图像中局部曝光不一致的挑战。具体而言，所提框架包含两个组件：局部对比度增强网络（LCEN）与全局光照引导网络（GIGN）。我们在LCEN中引入早停机制，并设计局部判别模块，该模块自适应感知图像中不同区域的对比度，以控制针对不同曝光水平图像块的增强过程过早终止。此外，在GIGN内部，我们设计了全局注意力引导模块，通过捕获图像中的长程依赖关系与上下文信息，有效建模全局光照分布，从而引导局部对比度增强网络显著提升不同区域的亮度。最后，为协调LCEN与GIGN的工作，我们设计了一种新颖的训练策略以优化训练过程。在多个数据集上的实验表明，相较于现有先进算法，本方法在定量与定性评估中均取得了更优的结果。