Low-light image enhancement (LLIE) is an ill-posed inverse problem due to the lack of knowledge of the desired image which is obtained under ideal illumination conditions. Low-light conditions give rise to two main issues: a suppressed image histogram and inconsistent relative color distributions with low signal-to-noise ratio. In order to address these problems, we propose a novel approach named FLIGHT-Net using a sequence of neural architecture blocks. The first block regulates illumination conditions through pixel-wise scene dependent illumination adjustment. The output image is produced in the output of the second block, which includes channel attention and denoising sub-blocks. Our highly efficient neural network architecture delivers state-of-the-art performance with only 25K parameters. The method's code, pretrained models and resulting images will be publicly available.

翻译：低光照图像增强（LLIE）是一个病态逆问题，因为无法获取理想光照条件下所得目标图像的信息。低光照条件引发两个主要问题：图像直方图受到抑制，以及信噪比低导致的相对颜色分布不一致。为解决这些问题，我们提出一种名为FLIGHT-Net的新方法，该方法采用一系列神经网络架构模块。第一个模块通过像素级场景相关光照调整来调节照明条件，第二个模块的输出生成最终图像，其中包含通道注意力与去噪子模块。我们高效设计的神经网络架构仅需25K参数即可实现最先进的性能。该方法的代码、预训练模型及生成图像将公开发布。