Inversion Restoring Algorithm for Whiskbroom Scanning Images Synthesized with Deep Convolutional Neural Network

doi:10.3788/AOS201939.1228001

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Inversion Restoring Algorithm for Whiskbroom Scanning Images Synthesized with Deep Convolutional Neural Network
	C.Xu; G.Jin; X.Yang; T.Xu; L.Chang
刊名	Guangxue Xuebao/Acta Optica Sinica
	2019
卷号	39 期号:12
关键词	Image enhancement,Cameras,Convolution,Deep neural networks,Deterioration,Distortion (waves),Geometry,Image quality,Image reconstruction,Network architecture,Neural networks,Pixels,Quality control,Remote sensing,Restoration,Scanning,Space optics
ISSN号	02532239
DOI	10.3788/AOS201939.1228001
英文摘要	To overcome the limitation of distortion and quality deterioration in whiskbroom scanning images, we propose a geometric correction and image enhancement method that combines the resolution inversion with deep convolutional neural network (DCNN) architecture. During the whiskbroom scanning process, the total whiskbroom scanning angle and unit field of view angle of a space camera are invariable, and each pixel of the detector on the image plane corresponds to the ground scene pointed by the camera boresight. Suitably, these help in restoring compressed pixels accurately. Furthermore, we adopt real-scene remote sensing panchromatic images as the sample to train the DCNN for remote sensing panchromatic images. Then, image blurring during the process of inversion is solved, and the visual effect of the corrected image is enhanced. In our experiment, the distortion corrected imagery restores the geometric characteristics of the ground scene to a large extent. The no-reference image quality evaluation indicators are used to evaluate our proposed network architecture, network trained on generic image set and interpolation method. The experimental result indicates that our proposed network realizes the best performance of image enhancement among the three methods with a great restoration effect of the whiskbroom scanning images. 2019, Chinese Lasers Press. All right reserved.
URL标识	查看原文
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/62920]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	C.Xu,G.Jin,X.Yang,et al. Inversion Restoring Algorithm for Whiskbroom Scanning Images Synthesized with Deep Convolutional Neural Network[J]. Guangxue Xuebao/Acta Optica Sinica,2019,39(12).
APA	C.Xu,G.Jin,X.Yang,T.Xu,&L.Chang.(2019).Inversion Restoring Algorithm for Whiskbroom Scanning Images Synthesized with Deep Convolutional Neural Network.Guangxue Xuebao/Acta Optica Sinica,39(12).
MLA	C.Xu,et al."Inversion Restoring Algorithm for Whiskbroom Scanning Images Synthesized with Deep Convolutional Neural Network".Guangxue Xuebao/Acta Optica Sinica 39.12(2019).