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

Abstract

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.

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“…One of the first approaches based on a GAN was SRGAN [33]. Other GAN-based SR methods include EEGAN [34], ESRGAN [35], ESRGAN+ [36], EnhanceNet [37] and OpTiGAN [38]. GANs are also used for SR for EO tasks: MA-GAN [39] combines a GAN with multi-attention and a pyramidal structure; TE-SAGAN [40] reduces artefacts and improves texture with self-attention and weight normalisation; NDSRGAN [41] uses pairs of images taken at different altitudes instead of bicubically downsampled images.…”

Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration

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