基于GAN的无人机航拍图像重建

doi:10.16180/j.cnki.issn1007-7820.2019.08.013

Abstract

Abstract:

The amount of data transmitted during the traditional UAV acquisition and transmission process often resulted in high consumption of the UAV battery. Aiming at solving the problem, a CsRGAN model that combined super-resolution reconstruction and gray-scale image colorization was proposed. The low resolution grayscale image was reconstructed by generating a network: the image was first subjected to resolution amplification, color filling was performed, and then the image was corrected by the discriminator, and finally the image was reconstructed into a color high-definition image. The experimental results showed that under the fixed area, the model could reduce the transmission data of the drone aerial photography and improve the battery utilization of the drone under the condition of ensuring the imaging quality. These results proved the model had strong robustness.

Key words: UVA, super-resolution, image colorization, image reconstruction, GAN, fix area

CLC Number:

TP391

CAO Kun,WU Fei,QIAN Xiaorui,YANG Zhaokun. Aerial Image Reconstruction of Drone Based on GAN[J].Electronic Science and Technology, 2019, 32(8): 61-65.

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References 16

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Loss	Per-pixel acc.	Per-class acc.	Class IOU
L₁+GAN	0.68	0.19	0.14
L₁+GAN+L_HSV	0.70	0.22	0.15

序列	输入图像/kB	输出图像/kB	差值倍数
15	27	875	32.4
30	28	935	33.4
81	39	1 356	34.8
130	30	1 057	35.2

序列	保真性PSNR	鲁棒性NC	结构相似性SSIM
15	28.617 190	0.997 791	0.981 915
30	28.815 120	0.997 691	0.981 965
81	27.958 641	0.997 781	0.982 935
130	21.758 541	0.997 715	0.981 915

方法	数据量 /kB·s^-1	传输视频分辨率	播放视频分辨率	300帧视频传输时间/s
文献[15]	10 640.228	640×480	640×480	20.66
文献[16]	12 702.890	640×480	640×480	17.31
本文方法	10 640.228	256×256	1024×1024	2.25

Aerial Image Reconstruction of Drone Based on GAN

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