Application of filtering efficiency prediction to hyperspectral data pre-processing
Several approaches to prediction image denoising efficiency for DCT-based filter have been proposed recently. They allow predicting improvement of PSNR (IPSNR) and visual quality metrics as PSNR-HVS-M (IPHVS) for denoised images under condition of noise characteristics known or pre-estimated in adva...
Збережено в:
| Дата: | 2015 |
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| Автори: | , , , , , , , |
| Формат: | Стаття |
| Мова: | Англійська |
| Опубліковано: |
Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
2015
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| Теми: | |
| Онлайн доступ: | https://ujrs.org.ua/ujrs/article/view/58 |
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| Назва журналу: | Ukrainian Journal of Remote Sensing of the Earth |
Репозитарії
Ukrainian Journal of Remote Sensing of the Earth| Резюме: | Several approaches to prediction image denoising efficiency for DCT-based filter have been proposed recently. They allow predicting improvement of PSNR (IPSNR) and visual quality metrics as PSNR-HVS-M (IPHVS) for denoised images under condition of noise characteristics known or pre-estimated in advance. Here we apply the prediction approach to pre-processing ten sub-bands of Hyperion hyperspectral data. It is shown that there are sub-band images for which there is no necessity to carry out filtering. Meanwhile, there are sub-bands for which IPSNR reaches 5…9 dB and the use of denoising is expedient. |
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