In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission
Atal Tewari, Chennuri Prateek, and Nitin Khanna

TL;DR
This paper presents a novel in-orbit satellite image super-resolution system that improves image quality and reduces computational resource usage, enabling selective data transmission and efficient satellite data utilization.
Contribution
The paper introduces a new residual dense non-local attention network and a specialized loss function for in-orbit super-resolution on low-power devices, enhancing image quality and efficiency.
Findings
Outperforms RDN in PSNR by +0.1 dB and +0.19 dB in block-sensitive PSNR.
Consumes 48% less memory and 67% less peak power than standard RDN.
Demonstrates effectiveness on Kaguya DOMs for satellite image super-resolution.
Abstract
Rapid technological advancements have tremendously increased the data acquisition capabilities of remote sensing satellites. However, the data utilization efficiency in satellite missions is very low. This growing data also escalates the cost required for data downlink transmission and post-processing. Selective data transmission based on in-orbit inferences will address these issues to a great extent. Therefore, to decrease the cost of the satellite mission, we propose a novel system design for selective data transmission, based on in-orbit inferences. As the resolution of images plays a critical role in making precise inferences, we also include in-orbit super-resolution (SR) in the system design. We introduce a new image reconstruction technique and a unique loss function to enable the execution of the SR model on low-power devices suitable for satellite environments. We present a…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Image and Signal Denoising Methods
