噪声指导下过滤光照风格实现低光照场景的语义分割

低光照图像分割一直是图像分割的难点，低光照引起的低对比度和高模糊性使得这类图像分割比一般图像分割困难很多。为了提高低光照环境下语义分割的准确度，根据低光照图像自身特征，提出一种噪声指导下过滤光照风格的低光照场景语义分割模型（SFIS）。该模型综合利用信噪比作为先验知识，通过指导长距离分支中的自注意力操作、长/短距离分支的特征融合，对图像中不同噪声的区域采用不同距离的交互。还进一步设计了一个光照过滤器，该模块从图像的整体风格中进一步提取光照风格信息。通过交替训练光照过滤器与语义分割模型，逐步减小不同光照条件之间的光照风格差距，从而使分割网络学习到光照不变特征。提出的模型在数据集LLRGBD上优于之前的工作，取得了较好的结果。在真实数据集LLRGBD-real上的mIoU达到66.8%，说明所提出的长短距离分支模块和光照过滤器模块能够有效提升模型在低光照环境下的语义分割能力。

Filtering illumination style under guidance of noise to achieve semantic segmentation of low-light scenes

Low-light image segmentation is always the difficulty of image segmentation. The low contrast and high fuzziness caused by low light make this kind of image segmentation much more difficult than general image segmentation. In order to improve the accuracy of semantic segmentation in low light environment, this paper proposed a semantic segmentation model of low light scene with filtering light style under noise guidance(SFIS) according to the characteristics of low-light image. The model comprehensively used signal-to-noise ratio as prior knowledge, and adopted different distance interaction for different noise regions in the image by guiding the self-attention operation in the long distance branch and the feature fusion of long/short distance branches. This paper also further designed an illumination filter, which was a module that further extracted the illumination style information from the overall style of the image. By alternately training the illumination filter and the semantic segmentation model, the lighting style gap between different lighting conditions was gradually reduced, so that the segmentation network could learn illumination invariant features. The proposed model outperforms the previous work on the dataset LLRGBD and achieves the best results. The mIoU on the real dataset LLRGBD-real reaches 66.8%, it shows that the proposed long and short distance branch module and the illumination filter module can effectively improve the semantic segmentation ability of the model in low light environment.