1 Introduction

Imaging spectroscopy devices can capture an information-rich representation of the scene comprised by tens or hundreds of wavelength-indexed bands. In contrast with their trichromatic (colour) counterparts, these images are composed of as many channels, each of these corresponding to a particular narrow-band segment of the electromagnetic spectrum [1]. Thus, imaging spectroscopy has numerous applications in areas such as remote sensing [2,3], disease diagnosis and image-guided surgery [4], food monitoring and safety [5], agriculture [6], archaeological conservation [7], astronomy [8] and face recognition [9].

Recent advances in imaging spectroscopy have seen the development of sensors where the spectral filters are fully integrated into the complementary metal-oxide-semiconductor (CMOS) or charge-coupled device (CCD) detectors. These are multi-spectral imaging devices which are single-shot and offer numerous advantages in terms of speed of acquisition and form-factor [10,11]. However, one of the main drawbacks of these multispectral systems is the low raw spatial resolution per wavelength-indexed band in the image. Hence, super-resolving spectral images is crucial to achieving a much improved spatial resolution in these devices.

2 StereoMSI Dataset

As mentioned above, here we propose the StereoMSI dataset. The dataset is a novel RGB-spectral stereo image dataset for benchmarking example-based single spectral image and example-based RGB-guided spectral image super-resolution methods. The dataset is developed for research purposes only.

The 350 stereo pair images were collected from a diverse range of scenery in the city of Canberra, the capital of Australia. The nature of the images ranges from open industrial to office environments and from deserts to rainforests. In Figures 2 and 3 we display validation images for the former and latter, respectively.

2.1 Diversity and Resolution

During acquisition time, we paid particular attention to the exposure time and image quality as the stereo pairs were captured using different cameras. One is an RGB XiQ camera model MQ022CG-CM and the other is a XiQ multispectral camera model MQ022HG-IM-SM4x4 covering the interval [470, 620nm] in the visible spectral range.

2.2 Structure and Splits

The original spectral images were processed and cropped to the resolution 480240 so as to allow the stereo RGB images to be resized to a resolution 2 times larger in each axis, that is 960480. This is due to the fact that, in practice, the RGB camera used, based upon a CMOS image sensor, has a 22 Bayer RGGB pattern whereas the IMEC spectral sensors have a 44 pattern delivering 16 wavelength bands. Hence, the resolution of the RGB images in each axis is twice that of the spectral images.

3 Final Thoughts

In this paper we introduce a novel dataset of colour-multispectral images which we name StereoMSI. Unlike the above two RGB-NIR datasets, the dataset was primarily developed for the PIRM2018 spectral SR challenge and comprised 350 registered stereo RGB-spectral image pairs. The StereoMSI dataset is large enough to help develop deep learning spectral super-resolution methods and is, to the best of our knowledge, the first of its kind.