Images captured under low-light scenarios often suffer from low quality.
Previous CNN-based deep learning methods often involve using Retinex theory.
Nevertheless, most of them cannot perform well in more complicated datasets
like LOL-v2 while consuming too much computational resources. Besides, some of
these methods require sophisticated training at different stages, making the
procedure even more time-consuming and tedious. In this paper, we propose a
more accurate, concise, and one-stage Retinex theory based framework, RSEND.
RSEND first divides the low-light image into the illumination map and
reflectance map, then captures the important details in the illumination map
and performs light enhancement. After this step, it refines the enhanced
gray-scale image and does element-wise matrix multiplication with the
reflectance map. By denoising the output it has from the previous step, it
obtains the final result. In all the steps, RSEND utilizes Squeeze and
Excitation network to better capture the details. Comprehensive quantitative
and qualitative experiments show that our Efficient Retinex model significantly
outperforms other CNN-based models, achieving a PSNR improvement ranging from
0.44 dB to 4.2 dB in different datasets and even outperforms transformer-based
models in the LOL-v2-real dataset.

在本文中，我们提出了基于 RSEND 的更为准确、简洁和单阶段的 Retinex 理论框架，该框架首先将低光照图像分解为光照图和反射图，然后捕捉光照图中重要的细节并进行光照增强，在此步骤之后，优化增强后的灰度图像并与反射图进行逐元素矩阵乘法运算，通过对前一步骤输出进行去噪，最终获得最终结果。全面的定量和定性实验表明，我们的高效 Retinex 模型明显优于其他基于 CNN 的模型，在不同数据集中的 PSNR 提升范围从 0.44 dB 到 4.2 dB，甚至在 LOL-v2-real 数据集中优于基于 Transformer 的模型。

RSEND：基于 Retinex 和自适应低亮度区域检测的挤压与激励网络用于高效低光图像增强

RSEND: Retinex-based Squeeze and Excitation Network with Dark Region  Detection for Efficient Low Light Image Enhancement

Object detection in low-light scenarios has attracted much attention in the
past few years. A mainstream and representative scheme introduces enhancers as
the pre-processing for regular detectors. However, because of the disparity in
task objectives between the enhancer and detector, this paradigm cannot shine
at its best ability. In this work, we try to arouse the potential of enhancer +
detector. Different from existing works, we extend the illumination-based
enhancers (our newly designed or existing) as a scene decomposition module,
whose removed illumination is exploited as the auxiliary in the detector for
extracting detection-friendly features. A semantic aggregation module is
further established for integrating multi-scale scene-related semantic
information in the context space. Actually, our built scheme successfully
transforms the "trash" (i.e., the ignored illumination in the detector) into
the "treasure" for the detector. Plenty of experiments are conducted to reveal
our superiority against other state-of-the-art methods. The code will be public
if it is accepted.

通过引入增强器和探测器相结合的模式，本文试图激发增强器 + 探测器的潜能，通过利用去除光照的辅助信息来提取适合检测的特征，并建立语义聚合模块来融合多尺度场景相关的语义信息。进行了大量实验验证了我们方法在其他最先进的方法上的优越性。

废物变宝藏：通过分解和聚合实现低光物体检测

Trash to Treasure: Low-Light Object Detection via  Decomposition-and-Aggregation

Robotic Perception in diverse domains such as low-light scenarios, where new
modalities like thermal imaging and specialized night-vision sensors are
increasingly employed, remains a challenge. Largely, this is due to the limited
availability of labeled data. Existing Domain Adaptation (DA) techniques, while
promising to leverage labels from existing well-lit RGB images, fail to
consider the characteristics of the source domain itself. We holistically
account for this factor by proposing Source Preparation (SP), a method to
mitigate source domain biases. Our Almost Unsupervised Domain Adaptation (AUDA)
framework, a label-efficient semi-supervised approach for robotic scenarios --
employs Source Preparation (SP), Unsupervised Domain Adaptation (UDA) and
Supervised Alignment (SA) from limited labeled data. We introduce
CityIntensified, a novel dataset comprising temporally aligned image pairs
captured from a high-sensitivity camera and an intensifier camera for semantic
segmentation and object detection in low-light settings. We demonstrate the
effectiveness of our method in semantic segmentation, with experiments showing
that SP enhances UDA across a range of visual domains, with improvements up to
40.64% in mIoU over baseline, while making target models more robust to
real-world shifts within the target domain. We show that AUDA is a
label-efficient framework for effective DA, significantly improving target
domain performance with only tens of labeled samples from the target domain.

本文提出了 Source Preparation 方法用于减轻源域偏差，并将其与无监督域适应和监督对齐相结合，提出了使用少量标注数据的机器人场景的标签有效的半监督方法。在低光照场景下进行了大量的实场图像配对实验，结果表明该方法在语义分割方面的有效性，提高了近 40.64％的 mIoU，使目标模型更加健壮。 AUDA 是有效的 DA 标签节约框架，只需从目标域中获得十几个标记样本即可大大提高目标域性能。