Low-Light Image Enhancement (LLIE) aims to improve the perceptual quality of
an image captured in low-light conditions. Generally, a low-light image can be
divided into lightness and chrominance components. Recent advances in this area
mainly focus on the refinement of the lightness, while ignoring the role of
chrominance. It easily leads to chromatic aberration and, to some extent,
limits the diverse applications of chrominance in customized LLIE. In this
work, a ``brighten-and-colorize'' network (called BCNet), which introduces
image colorization to LLIE, is proposed to address the above issues. BCNet can
accomplish LLIE with accurate color and simultaneously enables customized
enhancement with varying saturations and color styles based on user
preferences. Specifically, BCNet regards LLIE as a multi-task learning problem:
brightening and colorization. The brightening sub-task aligns with other
conventional LLIE methods to get a well-lit lightness. The colorization
sub-task is accomplished by regarding the chrominance of the low-light image as
color guidance like the user-guide image colorization. Upon completion of model
training, the color guidance (i.e., input low-light chrominance) can be simply
manipulated by users to acquire customized results. This customized process is
optional and, due to its decoupled nature, does not compromise the structural
and detailed information of lightness. Extensive experiments on the commonly
used LLIE datasets show that the proposed method achieves both State-Of-The-Art
(SOTA) performance and user-friendly customization.

提出了一种称为 BCNet 的 “明亮和着色” 网络，通过引入图像着色来解决低光条件下图像增强的问题，并实现基于用户偏好的不同饱和度和颜色风格的定制增强。

亮度增强和着色：用于定制化低光图像增强的分离网络

Brighten-and-Colorize: A Decoupled Network for Customized Low-Light  Image Enhancement

Research on face spoofing detection has mainly been focused on analyzing the
luminance of the face images, hence discarding the chrominance information
which can be useful for discriminating fake faces from genuine ones. In this
work, we propose a new face anti-spoofing method based on color texture
analysis. We analyze the joint color-texture information from the luminance and
the chrominance channels using a color local binary pattern descriptor. More
specifically, the feature histograms are extracted from each image band
separately. Extensive experiments on two benchmark datasets, namely CASIA face
anti-spoofing and Replay-Attack databases, showed excellent results compared to
the state-of-the-art. Most importantly, our inter-database evaluation depicts
that the proposed approach showed very promising generalization capabilities.

通过颜色纹理分析的局部二值模式描述符的使用，来分析亮度和色度通道的结合颜色 - 纹理信息，提出了一种新的基于颜色纹理分析的人脸反欺诈方法，并在 CASIA 人脸反欺诈和 Replay-Attack 数据库上进行了广泛实验，表明其较现有技术具有更好的检测准确性。