Edge-Computing-Enabled Deep Learning Approach for Low-Light Satellite Image Enhancement
| Autor: | Trong-An Bui, Pei-Jun Lee, Chun-Sheng Liang, Pei-Hsiang Hsu, Shiuan-Hal Shiu, Chen-Kai Tsai |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 4071-4083 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1939-1404 2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3357093 |
| Popis: | Edge computing enables rapid data processing and decision-making on satellite payloads. Deploying deep learning-based techniques for low-light image enhancement improves early detection and tracking accuracy on satellite platforms, but it faces challenges due to limited computational resources. This article proposes an edge-computing-enabled inference model specifically designed onboard satellites. The proposed model follows an encoder–decoder architecture to generate the illumination map with low multiplication matrix complexity, 25.52 GMac of $1920 \times 1200$ image size. To reduce nanosatellite hardware consumption with a single-precision floating-point format, the edge-computing-enabled inference model proposes a quantized convolution that computes signed values. The proposed inference model is deployed on Arm Cortex-M3 microcontrollers onboard satellite payload (86.74 times faster than normal convolution model) but also has a similar quality with the low-light enhanced in full-precision computing of lightweight training model by using the peak signal-to-noise ratio (average of 28.94) and structural similarity index (average of 0.85) metrics. |
| Databáze: | Directory of Open Access Journals |
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