论文信息 - Super-Resolution Restoration of Spaceborne Ultra-High-Resolution Images Using the UCL OpTiGAN System

Super-Resolution Restoration of Spaceborne Ultra-High-Resolution Images Using the UCL OpTiGAN System

We introduce a robust and light-weight multi-image super-resolution restoration (SRR) method and processing system, called OpTiGAN, using a combination of a multi-image maximum a posteriori approach and a deep learning approach. We show the advantages of using a combined two-stage SRR processing scheme for significantly reducing inference artefacts and improving effective resolution in comparison to other SRR techniques. We demonstrate the optimality of OpTiGAN for SRR of ultra-high-resolution satellite images and video frames from 31 cm/pixel WorldView-3, 75 cm/pixel Deimos-2 and 70 cm/pixel SkySat. Detailed qualitative and quantitative assessments are provided for the SRR results on a CEOS-WGCV-IVOS geo-calibration and validation site at Baotou, China, which features artificial permanent optical targets. Our measurements have shown a 3.69 times enhancement of effective resolution from 31 cm/pixel WorldView-3 imagery to 9 cm/pixel SRR.

Jan-Peter Muller | Yu Tao | J. Muller | Yi Tao

[1] Ning Xu,et al. Wide Activation for Efficient and Accurate Image Super-Resolution , 2018, ArXiv.

[2] Alexia Jolicoeur-Martineau,et al. The relativistic discriminator: a key element missing from standard GAN , 2018, ICLR.

[3] Chaofeng Wang,et al. Lightweight Image Super-Resolution with Adaptive Weighted Learning Network , 2019, ArXiv.

[4] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5] Gunnar Farnebäck,et al. Two-Frame Motion Estimation Based on Polynomial Expansion , 2003, SCIA.

[6] Xiaoou Tang,et al. Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.