Astronomical image denoising based on Convolutional Neural Network
Abstrak
Astronomical images captured using optical telescopes usually suffer from severe noise effects which makes the denoising step inevitable for image analysis. This paper proposes a denoising framework for astronomical images based on Convolutional Neural Network (Astro U-net). The modified Astro U-net model has been learned in four ways, the first method is using astronomical images from the Hubble Space Telescope data set with three types of noise (dark noise, read- out noise, shot noise) added, the second method is learned using the same data set with the dark noise (dn) added only, the third method is using the same data set with the read-out noise (ron) overlaid, the fourth method is using the same data set with the shot noise (sn) added. The proposed framework for denoising the astronomical images is based on a fusion of the image that was improved by the model learned in the first method with the image that was improved by the three models that were learned by the second, third and fourth methods sequentially. Experimentally, the proposed framework shows a significant improvement in both the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) as compared to the Astro U-net model on different exposure time ratios.
Penulis (3)
A. A. Elhakiem
Tarek Elsaid Ghoniemy
G. Salama
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2021
- Bahasa
- en
- Total Sitasi
- 3×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/ICICIS52592.2021.9694140
- Akses
- Open Access ✓