基于预检机制的偏振图像去烟研究

Polarization image smoke removal based on precheck mechanism

烟的存在会导致图像目标信息的损减或丢失。针对烟在场景中具有局域性,提出了基于目标检测Yolov3算法的去烟预检机制,即在去烟流程中增加预检机制实现对烟图定向去烟,提升去烟效率和避免对无烟区的影响。不同于现有针对可见光图像的基于深度学习去雾方法,该方法将四幅偏振态图像作为网络输入,并利用多尺度注意力对抗网络提取烟区目标的偏振态特征信息,从而缓解失真现象以及丰富去烟后目标的结构和细节信息。在真实数据集上的定性与定量实验结果表明,本文提出的算法有效提升了偏振图像的去烟效果和去烟效率。

The presence of smoke can cause the damage or loss of image target information.In view of the local nature of smoke in the scene,a smoke removal precheck mechanism based on the target detection Yolov3 algorithm is proposed in this work,that is,a precheck mechanism is added in the smoke removal process to realize the directional removal of smoke on the smoke image,improve the efficiency of smoke removal and avoid the impact of smoke on the non-smoking area.Different from the existing deep learningbased defogging methods for visible images,this method takes four polarization images as network input,and uses multi-scale attention adversarial network to extract the polarization information of the target in thesmoke area, so as to alleviate distortion and enrich the structure and detail information of the target aftersmoke removal. Qualitative and quantitative experimental results on real data sets show that the proposedalgorithm can effectively improve the smoke removal effectiveness and efficiency of polarized images.