基于网格网络的大气湍流退化图像复原

Atmospheric Turbulence Degradation Image Restoration Based on Grid Network

大气湍流会导致图像发生退化。针对单幅大气湍流退化图像,提出基于网格网络的大气湍流退化图像复原方法。为了实现局部和深层次的多尺度特征提取,在主干模块中采用空洞卷积扩大模型感受野,同时在后处理模块中加入空间注意力模块,以更好地处理复原图像的白斑和伪影,提升像质。实验结果表明:所提网络平均0.29 s快速输出复原结果;相比其他方法,动态场景下对模拟数据的平均峰值信噪比(PSNR)和结构相似性(SSIM)最大提升高达9.44 dB和0.1173,同时对真实场景下的大气湍流复原也有较好效果。

Atmospheric turbulence causes image degradation.For a single degraded image of atmospheric turbulence,an image restoration method based on grid networks was proposed in this study.To realize local and deep multiscale feature extraction,dilated convolution was used in the backbone module to expand the model sensory field.Additionally,a spatial attention module was added to the postprocessing module.This enabled to better deal with the white spots and artifacts in the restored image and improve image quality.Experimental results show that the proposed network quickly outputs recovery results,demonstrating an average restoration output time of 0.29 s,and the average peak signaltonoise ratio(PSNR)and structural similarity(SSIM)of the simulated data obtained using the proposed algorithm in a dynamic scene are maximally improved up to 9.44 dB and 0.1173,respectively,compared with other methods.Furthermore,the algorithm exhibits better effect for recovering atmospheric turbulence in real scenes.