Single Image Super-Resolution Restoration of TGO CaSSIS Colour Images: Demonstration with Perseverance Rover Landing Site and Mars Science Targets

Abstract

The ExoMars Trace Gas Orbiter (TGO)’s Colour and Stereo Surface Imaging System (CaSSIS) provides multi-spectral optical imagery at 4-5m/pixel spatial resolution. Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted. In this work, we propose a novel Multi-scale Adaptive weighted Residual Super-resolution Generative Adversarial Network (MARSGAN) for single-image super-resolution restoration of TGO CaSSIS images, and demonstrate how this provides an effective resolution enhancement factor of about 3 times. We demonstrate with qualitative and quantitative assessments of CaSSIS SRR results over the Mars2020 Perseverance rover’s landing site. We also show examples of similar SRR performance over 8 science test sites mainly selected for being covered by HiRISE at higher resolution for comparison, which include many features unique to the Martian surface. Application of MARSGAN will allow high resolution colour imagery from CaSSIS to be obtained over extensive areas of Mars beyond what has been possible to obtain to date from HiRISE.

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

“…(c) Thirdly, we replace the original GAN prediction (refinement) module with the MARS-GAN model described in [43], where we demonstrated state-of-the-art single image SRR performance for 4 m/pixel Mars imaging datasets. The network architecture of MARSGAN can be found in [43]. In this work, we re-train the MARSGAN network with the same training dataset used in [44].…”

“…On the other hand, in the second stage of OpTiGAN processing, we replace the original GAN implementation, which was an optimised version of SRGAN [4], with our recently developed MARSGAN model [43]. This is the same as a general GAN framework, wherein MARSGAN trains a generator network to generate potential SRR solutions and a relativistic adversarial network [32,43,45] to pick-up the most realistic SRR solution.…”

“…On the other hand, in the second stage of OpTiGAN processing, we replace the original GAN implementation, which was an optimised version of SRGAN [4], with our recently developed MARSGAN model [43]. This is the same as a general GAN framework, wherein MARSGAN trains a generator network to generate potential SRR solutions and a relativistic adversarial network [32,43,45] to pick-up the most realistic SRR solution. MARSGAN uses 23 Adaptive Weighted Residual-in-Residual Dense Blocks (AWRRDBs), followed by an Adaptive Weighted Multi-Scale Reconstruction (AWMSR) block in the generator network, providing much higher network capacity and better performance, compared to the original SRGAN-based model used in MAGiGAN (see Section 3 for comparisons).…”

“…MARSGAN uses 23 Adaptive Weighted Residual-in-Residual Dense Blocks (AWRRDBs), followed by an Adaptive Weighted Multi-Scale Reconstruction (AWMSR) block in the generator network, providing much higher network capacity and better performance, compared to the original SRGAN-based model used in MAGiGAN (see Section 3 for comparisons). Besides, MARSGAN uses a balanced PSNR-driven and perceptual quality-driven [43] loss function to produce high quality restoration while limiting synthetic artefacts. A simplified network architecture of the MARSGAN model is shown in Figure 4.…”

“…A simplified network architecture of the MARSGAN model is shown in Figure 4. A detailed description of the MARSGAN network can be found in [43].…”