Abstract
Under extreme low-light conditions, traditional frame-based cameras, due to their limited dynamic range and temporal resolution, face detail loss and motion blur in captured images. To overcome this bottleneck, researchers have introduced event cameras and proposed event-guided low-light image enhancement algorithms. However, these methods neglect the influence of global low-frequency noise caused by dynamic lighting conditions and local structural discontinuities in sparse event data. To address these issues, we propose an innovative Bidirectional guided Low-light Image Enhancement framework (BiLIE). Specifically, to mitigate the significant low-frequency noise introduced by global illumination step changes, we introduce the frequency high-pass filtering-based Event Feature Enhancement (EFE) module at the event representation level to suppress the interference of low-frequency information, and preserve and highlight the high-frequency this http URL, we design a Bidirectional Cross Attention Fusion (BCAF) mechanism to acquire high-frequency structures and edges while suppressing structural discontinuities and local noise introduced by sparse event guidance, thereby generating smoother fused this http URL, considering the poor visual quality and color bias in existing datasets, we provide a new dataset (RELIE), with high-quality ground truth through a reliable enhancement scheme. Extensive experimental results demonstrate that our proposed BiLIE outperforms state-of-the-art methods by 0.96dB in PSNR and 0.03 in LPIPS.
Abstract (translated)
在极端低光条件下，传统的帧基摄像头由于其动态范围和时间分辨率有限，在捕捉图像时会面临细节丢失和运动模糊的问题。为克服这一瓶颈，研究人员引入了事件相机，并提出了基于事件的低光照图像增强算法。然而，这些方法忽略了由动态照明条件引起的全局低频噪声以及稀疏事件数据中局部结构不连续性的影响。为了应对这些问题，我们提出了一种创新的双向引导低光图像增强框架（BiLIE）。 具体而言，为减少由于全球照明变化步长引入的重大低频噪声，我们在事件表示层面引入了基于频率高通滤波的事件特征增强（EFE）模块来抑制低频信息的干扰，并保留和突出高频细节。同时，我们设计了一种双向交叉注意融合机制(BCAF)，以获取高频结构和边缘，同时抑制稀疏事件引导所引入的局部噪声及结构不连续性，从而生成更加平滑的融合图像。 此外，考虑到现有数据集中存在的视觉质量差以及色彩偏差的问题，我们提供了一个新的数据集(RELIE)，通过可靠增强方案来确保高质量的真实标签。广泛的实验结果表明，我们的BiLIE框架在PSNR指标上比当前最佳方法高出0.96dB，在LPIPS指标上高出0.03。
URL
https://arxiv.org/abs/2506.06120
