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1. Multi-spectral super-resolution dataset (extension to PIRM2018 multi-spectral super-resolution challenge dataset).
The dataset includes
- 272 training stereo registered multi-spectral RGB image pairs including the original RGB mosaic images.
- 970 raw multi-spectral images. These need to be downsampled (x2 or x3 etc.) to create low-resolution im... moreages for training a CNN for the task of super-resolution.
- 50 test images.
2. Dataset for simultaneous colour-prediction and super-resolution.
This dataset includes:
- 250 registered stereo multi-spectral/RGB image pairs for training
- 25 registered stereo multi-spectral/RGB image pairs for validation
- 21 registered stereo multi-spectral/RGB image pairs for testing. less
Knowledge Representation and Machine Learning
1. Images in the multi-spectral super-resolution dataset are in .tiff format.
2. Images in the colour-prediction dataset are in .tiff (RGB) and .mat (multi-spectral) formats. Converting .mat to .npy can accelerate training. See https://arxiv.org/pdf/1909.02221.pdf for more information on multi-spectral data format.
CSIRO Data Licence
Shoeiby, Mehrdad (2019): StereoMSI. v2. CSIRO. Data Collection.
All Rights (including copyright) CSIRO 2019.
The metadata and files (if any) are available to the public.
AIM FSP_TB07_WP05: Optical sensing for marine environments
Data analytics for marine environments
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