In addition to low light, night images suffer degradation from light effects
(e.g., glare, floodlight, etc). However, existing nighttime visibility
enhancement methods generally focus on low-light regions, which neglects, or
even amplifies the light effects. To address this issue, we propose an Adaptive
Multi-scale Fusion network (AMFusion) with infrared and visible images, which
designs fusion rules according to different illumination regions. First, we
separately fuse spatial and semantic features from infrared and visible images,
where the former are used for the adjustment of light distribution and the
latter are used for the improvement of detection accuracy. Thereby, we obtain
an image free of low light and light effects, which improves the performance of
nighttime object detection. Second, we utilize detection features extracted by
a pre-trained backbone that guide the fusion of semantic features. Hereby, we
design a Detection-guided Semantic Fusion Module (DSFM) to bridge the domain
gap between detection and semantic features. Third, we propose a new
illumination loss to constrain fusion image with normal light intensity.
Experimental results demonstrate the superiority of AMFusion with better visual
quality and detection accuracy. The source code will be released after the peer
review process.

提出了一种自适应多尺度融合网络（AMFusion），通过红外和可见图像的融合规则，分别调整光照分布和提高检测准确性，从而实现针对夜晚物体检测的性能提升。

超越夜间可见性：红外和可见光图像的自适应多尺度融合

Beyond Night Visibility: Adaptive Multi-Scale Fusion of Infrared and  Visible Images

Night images suffer not only from low light, but also from uneven
distributions of light. Most existing night visibility enhancement methods
focus mainly on enhancing low-light regions. This inevitably leads to over
enhancement and saturation in bright regions, such as those regions affected by
light effects (glare, floodlight, etc). To address this problem, we need to
suppress the light effects in bright regions while, at the same time, boosting
the intensity of dark regions. With this idea in mind, we introduce an
unsupervised method that integrates a layer decomposition network and a
light-effects suppression network. Given a single night image as input, our
decomposition network learns to decompose shading, reflectance and
light-effects layers, guided by unsupervised layer-specific prior losses. Our
light-effects suppression network further suppresses the light effects and, at
the same time, enhances the illumination in dark regions. This light-effects
suppression network exploits the estimated light-effects layer as the guidance
to focus on the light-effects regions. To recover the background details and
reduce hallucination/artefacts, we propose structure and high-frequency
consistency losses. Our quantitative and qualitative evaluations on real images
show that our method outperforms state-of-the-art methods in suppressing night
light effects and boosting the intensity of dark regions.

该论文提出了一种无监督的方法，通过层分解网络和光效抑制网络集成来来压制夜间图像中光效，并增强暗区域的照度