Low-light image enhancement task is essential yet challenging as it is
ill-posed intrinsically. Previous arts mainly focus on the low-light images
captured in the visible spectrum using pixel-wise loss, which limits the
capacity of recovering the brightness, contrast, and texture details due to the
small number of income photons. In this work, we propose a novel approach to
increase the visibility of images captured under low-light environments by
removing the in-camera infrared (IR) cut-off filter, which allows for the
capture of more photons and results in improved signal-to-noise ratio due to
the inclusion of information from the IR spectrum. To verify the proposed
strategy, we collect a paired dataset of low-light images captured without the
IR cut-off filter, with corresponding long-exposure reference images with an
external filter. The experimental results on the proposed dataset demonstrate
the effectiveness of the proposed method, showing better performance
quantitatively and qualitatively. The dataset and code are publicly available
at this https URL

提出了一种通过去除相机红外 (IR) 截止滤波器来提高低光环境下图像可见度的新方法，实验结果定量和定性上显示了提出方法的有效性。

利用红外编码图像增强低光照图像

Enhancing Low-Light Images Using Infrared-Encoded Images

Adverse conditions typically suffer from stochastic hybrid weather
degradations (e.g., rainy and hazy night), while existing image restoration
algorithms envisage that weather degradations occur independently, thus may
fail to handle real-world complicated scenarios. Besides, supervised training
is not feasible due to the lack of comprehensive paired dataset to characterize
hybrid conditions. To this end, we have advanced the forementioned limitations
with two tactics: framework and data. On the one hand, we present a novel
unified framework, dubbed RAHC, to Restore Arbitrary Hybrid adverse weather
Conditions in one go, which can comfortably cope with hybrid scenarios with
insufficient remaining background constituents and restore arbitrary hybrid
conditions with a single trained model flexibly. On the other hand, we
establish a new dataset, termed HAC, for learning and benchmarking arbitrary
Hybrid Adverse Conditions restoration. HAC contains 31 scenarios composed of an
arbitrary combination of five common weather, with a total of ~316K
adverse-weather/clean pairs. As for fabrication, the training set is
automatically generated by a dedicated AdverseGAN with no-frills labor, while
the test set is manually modulated by experts for authoritative evaluation.
Extensive experiments yield superior results and in particular establish new
state-of-the-art results on both HAC and conventional datasets.

提出了一种新颖的框架 RAHC，用于恢复复杂的天气情况下的图像，同时建立了一个新的数据集 HAC，用于学习和基准测试不同的天气条件下的图像恢复方法。