DarkDeblur提升暗光单张图像去模糊效果，适合真实场景应用。
DarkDeblur: Learning single-shot image deblurring in low-light condition
- 采用特征金字塔中嵌入密集注意力与上下文门控机制
- 在合成与真实数据上均超越现有最佳方法
- 构建了真实硬件采集的基准数据集供评估
低光条件下的单张图像去模糊是一项极具挑战性的图像转换任务。本文提出一种基于学习的方法，设计名为DarkDeblurNet的新型深度网络。该网络在特征金字塔结构中引入密集注意力块和上下文门控机制，增强内容感知能力；同时采用多目标损失函数，在低光环境下兼顾感知质量与去模糊效果。所提模型已在多种计算机视觉应用中验证其实用性。此外，本文还构建了一个基于真实硬件采集的基准数据集，用于评估低光图像去模糊方法。实验结果表明，该方法在合成数据和真实数据上均优于当前最先进方法，即使在极端光照条件下仍表现优异。
原文摘要 · Abstract (English)
Single-shot image deblurring in a low-light condition is known to be a profoundly challenging image translation task. This study tackles the limitations of the low-light image deblurring with a learning-based approach and proposes a novel deep network named as DarkDeblurNet. The proposed DarkDeblur- Net comprises a dense-attention block and a contextual gating mechanism in a feature pyramid structure to leverage content awareness. The model additionally incorporates a multi-term objective function to perceive a plausible perceptual image quality while performing image deblurring in the low-light settings. The practicability of the proposed model has been verified by fusing it in numerous computer vision applications. Apart from that, this study introduces a benchmark dataset collected with actual hardware to assess the low-light image deblurring methods in a real-world setup. The experimental results illustrate that the proposed method can outperform the state-of-the-art methods in both synthesized and real-world data for single-shot image deblurring, even in challenging lighting environment.
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