功能解耦和谱特征融合的雪霾消除模型

Snow and Haze Elimination Model Based on Function Decoupling and Edge Feature Fusion

针对车载相机受雪花、雾霾影响,导致采集图像出现雪花遮挡和雾霾面纱效应问题,基于图像边缘纹理和图像色彩分离重建的思想,提出功能解耦、双重监督的雪霾消除网络。所提算法通过对图像边缘纹理和色彩信息进行分离重建,将雪霾消除任务解耦为背景纹理修复与色彩重建两个子任务,并用双生成对抗网络分别进行边缘纹理和色彩特征的协同重建。算法在SRRS-6000数据集上进行消融测试,验证了双重监督对网络加速收敛的有效性和噪声消除的显著效果,模型在Snow100K-S、Snow100K-M、Snow100K-L、I&O-Haze数据集上进行测试,峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structural similarity,SSIM)分别达到33.29 dB和0.94、32.8 dB和0.9316、30.13 dB和0.93、25.88 dB和0.82。实验结果表明,通过对图像去噪任务进行解耦和双重监督,取得了高效的雪花、雾霾消除效果,增强了无人驾驶辅助系统在复杂天气条件下的适应性。

Aiming at the problem of snowflake occlusion and haze veil effect in the collected images by the vehiclemounted camera for the influence of snowflakes and haze,this paper proposes a decoupling and double-supervised snow haze elimination network based on the idea of image edge texture and image color separation and reconstruction.The proposed algorithm separates and reconstructs image edge texture and color information,decouples the snow haze removal task into two sub-tasks,background texture inpainting and color reconstruction,and uses dual generative adversarial networks to reconstruct edge texture and color features separately.The algorithm has tested on the SRRS-6000 dataset,which verifies the effectiveness of dual supervision on network acceleration convergence and the significant effect for noise removal.The peak signal to noise ratio(PSNR)and structural similarity(SSIM)has reached 33.29 dB and 0.94,32.8 dB and 0.9316,30.13 dB and 0.93,25.88 dB and 0.82 on Snow100K-S,Snow100K-M,Snow100K-L and I&O-Haze datasets,respectively.Experimental results show that by method of decoupling and double supervision in image denoising task,efficient snowflake and haze removal performance have achieved,and the adaptability of unmanned assistance system under complex weather conditions have been enhanced.