Due to the limitations of devices, images taken in low-light environments are of low contrast and high noise without any manual intervention. Such images will affect the visual experience and hinder further visual processing tasks, such as target detection and target tracking. To alleviate this issue, we propose a multi-scale joint low-light enhancement network based on the Retinex theory. The network consists of a decomposition part and an enhancement part. As a joint network, the decomposition and enhancement parts are mutually constrained, and the parameters are updated at the same time so that the image processing results are more excellent in detail. Our algorithm avoids the separation and recombination of decomposition and enhancement. Therefore, less information is lost in the processing of low-light images, and the enhancement result of the proposed algorithm is very close to the ground truth. In addition, in the enhancement part, we adopt a multi-scale network to fully extract image features. The multi-scale network maintains a balance between the global and local luminance of the illumination image. Retinex theory can effectively solve the problem of noise amplification and color distortion. At the same time, we have added color loss to solve the problem of color distortion, so that the enhancement result is closer to the normal-light image in color. The enhancement results are intuitively excellent, and the peak signal-to-noise ratio and structural similarity index results also reflect the reliability of the algorithm.

由于设备的限制，在弱光环境下拍摄的图像对比度低且噪声高，无需任何人工干预。这样的图像将影响视觉体验并阻碍进一步的视觉处理任务，例如目标检测和目标跟踪。为了缓解这个问题，我们提出了一种基于Retinex理论的多尺度联合弱光增强网络。该网络由分解部分和增强部分组成。作为一个联合网络，分解和增强部分相互约束，并且参数同时更新，因此图像处理效果更加出色。我们的算法避免了分解和增强的分离和重组。因此，在弱光图像的处理中丢失的信息更少，所提算法的增强结果与地面实况非常接近。另外，在增强部分，我们采用了多尺度网络来完全提取图像特征。多尺度网络在照明图像的整体和局部亮度之间保持平衡。Retinex理论可以有效地解决噪声放大和色彩失真的问题。同时，我们增加了色彩损失以解决色彩失真的问题，从而使增强效果在色彩上更接近普通光图像。增强结果直观上非常出色，峰值信噪比和结构相似性指标结果也反映了算法的可靠性。我们采用多尺度网络来完全提取图像特征。多尺度网络在照明图像的整体和局部亮度之间保持平衡。Retinex理论可以有效地解决噪声放大和色彩失真的问题。同时，我们增加了色彩损失以解决色彩失真的问题，从而使增强效果在色彩上更接近普通光图像。增强结果直观上非常出色，峰值信噪比和结构相似性指标结果也反映了算法的可靠性。我们采用多尺度网络来完全提取图像特征。多尺度网络在照明图像的整体和局部亮度之间保持平衡。Retinex理论可以有效地解决噪声放大和色彩失真的问题。同时，我们增加了色彩损失以解决色彩失真的问题，从而使增强效果在色彩上更接近普通光图像。增强结果直观上非常出色，峰值信噪比和结构相似性指标结果也反映了算法的可靠性。我们增加了色彩损失以解决色彩失真的问题，从而使增强效果的色彩更接近正常光线的图像。增强结果直观上非常出色，峰值信噪比和结构相似性指标结果也反映了算法的可靠性。我们增加了色彩损失以解决色彩失真的问题，从而使增强效果的色彩更接近正常光线的图像。增强结果直观上非常出色，峰值信噪比和结构相似性指标结果也反映了算法的可靠性。