Low-light images, characterized by inadequate illumination, pose challenges of diminished clarity, muted colors, and reduced details. Low-light image enhancement, an essential task in computer vision, aims to rectify these issues by improving brightness, contrast, and overall perceptual quality, thereby facilitating accurate analysis and interpretation. This paper introduces the Convolutional Dense Attention-guided Network (CDAN), a novel solution for enhancing low-light images. CDAN integrates an autoencoder-based architecture with convolutional and dense blocks, complemented by an attention mechanism and skip connections. This architecture ensures efficient information propagation and feature learning. Furthermore, a dedicated post-processing phase refines color balance and contrast. Our approach demonstrates notable progress compared to state-of-the-art results in low-light image enhancement, showcasing its robustness across a wide range of challenging scenarios. Our model performs remarkably on benchmark datasets, effectively mitigating under-exposure and proficiently restoring textures and colors in diverse low-light scenarios. This achievement underscores CDAN's potential for diverse computer vision tasks, notably enabling robust object detection and recognition in challenging low-light conditions.

翻译：低光照图像因照明不足而呈现清晰度下降、色彩暗淡及细节缺失等挑战。低光照图像增强作为计算机视觉领域的基础任务，旨在通过改善亮度、对比度和整体感知质量来修正上述问题，从而促进精确分析与解译。本文提出卷积密集注意力引导网络（CDAN），这是一种创新的低光照图像增强方案。该网络融合了基于自编码器的架构与卷积块及密集块，并辅以注意力机制和跳跃连接，确保高效的信息传播与特征学习。此外，专用后处理阶段可优化色彩平衡与对比度。本方法在低光照图像增强领域相较现有最优结果展现出显著进步，在多种复杂场景下均具备鲁棒性。我们的模型在基准数据集上表现卓越，能有效抑制欠曝光问题，并在多样化的低光照场景中精准恢复纹理与色彩。这一成果彰显了CDAN在各类计算机视觉任务中的潜力，特别是在恶劣低光照条件下实现稳健的目标检测与识别。