Low-light scenes are prevalent in real-world applications (e.g. autonomous
driving and surveillance at night). Recently, multi-object tracking in various
practical use cases have received much attention, but multi-object tracking in
dark scenes is rarely considered. In this paper, we focus on multi-object
tracking in dark scenes. To address the lack of datasets, we first build a
Low-light Multi-Object Tracking (LMOT) dataset. LMOT provides well-aligned
low-light video pairs captured by our dual-camera system, and high-quality
multi-object tracking annotations for all videos. Then, we propose a low-light
multi-object tracking method, termed as LTrack. We introduce the adaptive
low-pass downsample module to enhance low-frequency components of images
outside the sensor noises. The degradation suppression learning strategy
enables the model to learn invariant information under noise disturbance and
image quality degradation. These components improve the robustness of
multi-object tracking in dark scenes. We conducted a comprehensive analysis of
our LMOT dataset and proposed LTrack. Experimental results demonstrate the
superiority of the proposed method and its competitiveness in real night
low-light scenes. Dataset and Code: https: //github.com/ying-fu/LMOT

本文介绍了一个低光场景下的多目标跟踪方法和相应的数据集 LMOT，证实了该方法在实际夜间低光场景中的竞争力。

暗光下的多目标追踪

Multi-Object Tracking in the Dark

As a neuromorphic sensor with high temporal resolution, spike camera can
generate continuous binary spike streams to capture per-pixel light intensity.
We can use reconstruction methods to restore scene details in high-speed
scenarios. However, due to limited information in spike streams, low-light
scenes are difficult to effectively reconstruct. In this paper, we propose a
bidirectional recurrent-based reconstruction framework, including a
Light-Robust Representation (LR-Rep) and a fusion module, to better handle such
extreme conditions. LR-Rep is designed to aggregate temporal information in
spike streams, and a fusion module is utilized to extract temporal features.
Additionally, we have developed a reconstruction benchmark for high-speed
low-light scenes. Light sources in the scenes are carefully aligned to
real-world conditions. Experimental results demonstrate the superiority of our
method, which also generalizes well to real spike streams. Related codes and
proposed datasets will be released after publication.

本文提出了一个双向循环基于重建框架的方法，包括一个强光鲁棒表示（LR-Rep）和融合模块，以更好地处理低光照条件下的场景重建问题。实验证明了该方法的优越性，也在真实的脉冲流中具有很好的泛化性。

从脉冲流中学习稳健地重建低光动态场景

Learning to Robustly Reconstruct Low-light Dynamic Scenes from Spike  Streams

Image/video denoising in low-light scenes is an extremely challenging problem
due to limited photon count and high noise. In this paper, we propose a novel
approach with contrastive learning to address this issue. Inspired by the
success of contrastive learning used in some high-level computer vision tasks,
we bring in this idea to the low-level denoising task. In order to achieve this
goal, we introduce a new denoising contrastive regularization (DCR) to exploit
the information of noisy images and clean images. In the feature space, DCR
makes the denoised image closer to the clean image and far away from the noisy
image. In addition, we build a new feature embedding network called Wnet, which
is more effective to extract high-frequency information. We conduct the
experiments on a real low-light dataset that captures still images taken on a
moonless clear night in 0.6 millilux and videos under starlight (no moon
present, <0.001 lux). The results show that our method can achieve a higher
PSNR and better visual quality compared with existing methods

本文通过对比学习的思想，提出了一种新的低光场景下图像 / 视频去噪方法，引入 DCR 措施去利用噪声图像与干净图像间的信息，以及利用新构建的特征网络 Wnet 提取高频信息，实验结果表明相较于现有的方法，该方法在 PSNR 以及视觉效果上取得了更好的表现。

低光源 RAW 图像去噪的对比学习

Contrastive Learning for Low-light Raw Denoising

This paper considers matching images of low-light scenes, aiming to widen the
frontier of SfM and visual SLAM applications. Recent image sensors can record
the brightness of scenes with more than eight-bit precision, available in their
RAW-format image. We are interested in making full use of such high-precision
information to match extremely low-light scene images that conventional methods
cannot handle. For extreme low-light scenes, even if some of their brightness
information exists in the RAW format images' low bits, the standard raw image
processing on cameras fails to utilize them properly. As was recently shown by
Chen et al., CNNs can learn to produce images with a natural appearance from
such RAW-format images. To consider if and how well we can utilize such
information stored in RAW-format images for image matching, we have created a
new dataset named MID (matching in the dark). Using it, we experimentally
evaluated combinations of eight image-enhancing methods and eleven image
matching methods consisting of classical/neural local descriptors and
classical/neural initial point-matching methods. The results show the advantage
of using the RAW-format images and the strengths and weaknesses of the above
component methods. They also imply there is room for further research.

本文研究如何利用更高精度的 RAW 格式图像匹配极低光场景的图像，并通过实验评估了图像增强和匹配方法的效果。