为立方星设计可自适应的图像处理系统，提升太空任务灵活性与鲁棒性。
Adaptive and Robust Image Processing on CubeSats
- 构建模块化流水线框架DIPP，支持部署后动态调整任务。
- DIPP降低更新管道的网络开销，抗错误模块上传能力强。
- 专用语言DISH比通用脚本更省内存，适合低资源设备。
立方星为地球观测等空间研究提供了低成本平台，但其资源受限且处于太空环境，对图像处理流程的灵活性和复杂性构成挑战。本文提出两个新系统：DIPP是一种模块化、可配置的图像处理流水线框架，可在部署后根据任务变化灵活调整，同时保持鲁棒性；DISH是一种领域特定语言（DSL）及运行时系统，用于在低功耗、内存受限的处理器上调度复杂成像工作负载。实验表明，DIPP对处理流程的分解几乎无额外开销，显著降低更新管道的网络需求，并具备抵抗错误模块上传的能力。此外，将DISH与通用脚本语言Lua对比，结果表明其表达能力相当，但内存占用更低。
原文摘要 · Abstract (English)
CubeSats offer a low-cost platform for space research, particularly for Earth observation. However, their resource-constrained nature and being in space, challenge the flexibility and complexity of the deployed image processing pipelines and their orchestration. This paper introduces two novel systems, DIPP and DISH, to address these challenges. DIPP is a modular and configurable image processing pipeline framework that allows for adaptability to changing mission goals even after deployment, while preserving robustness. DISH is a domain-specific language (DSL) and runtime system designed to schedule complex imaging workloads on low-power and memory-constrained processors. Our experiments demonstrate that DIPP's decomposition of the processing pipelines adds negligible overhead, while significantly reducing the network requirements of updating pipelines and being robust against erroneous module uploads. Furthermore, we compare DISH to Lua, a general purpose scripting language, and demonstrate its comparable expressiveness and lower memory requirement.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。