
Skyora is a mathematics-based astrophotography image enhancement system designed for low-end devices and embedded camera systems. Unlike AI-based tools, Skyora runs fully offline and improves lunar image quality by calculating moon parameters from image metadata (date, time, location) and blending high-quality moon images into the original. It also reduces noise with traditional denoising filters, runs efficiently on low-cost hardware, and is intended for amateur astrophotographers and hobbyists who do not have access to professional cameras. The paper includes methodology, example images, benchmarks against AI models, limitations, and potential future work.Keywords: astrophotography, image processing, lunar enhancement, optimization, low-cost systems, offline computer vision, embedded camera systems.
Image processing, low cost, Computer vision, astrophotography, Image Enhancement, optimization, embedded camera systems
Image processing, low cost, Computer vision, astrophotography, Image Enhancement, optimization, embedded camera systems
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
