TY - GEN
T1 - PIRM2018 challenge on spectral image super-resolution
T2 - 15th European Conference on Computer Vision, ECCV 2018
AU - Shoeiby, Mehrdad
AU - Robles-Kelly, Antonio
AU - Timofte, Radu
AU - Zhou, Ruofan
AU - Lahoud, Fayez
AU - Süsstrunk, Sabine
AU - Xiong, Zhiwei
AU - Shi, Zhan
AU - Chen, Chang
AU - Liu, Dong
AU - Zha, Zheng Jun
AU - Wu, Feng
AU - Wei, Kaixuan
AU - Zhang, Tao
AU - Wang, Lizhi
AU - Fu, Ying
AU - Nagasubramanian, Koushik
AU - Singh, Asheesh K.
AU - Singh, Arti
AU - Sarkar, Soumik
AU - Ganapathysubramanian, Baskar
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In this paper, we describe the Perceptual Image Restoration and Manipulation (PIRM) workshop challenge on spectral image super-resolution, motivate its structure and conclude on results obtained by the participants. The challenge is one of the first of its kind, aiming at leveraging modern machine learning techniques to achieve spectral image super-resolution. It comprises of two tracks. The first of these (Track 1) is about example-based single spectral image super-resolution. The second one (Track 2) is on colour-guided spectral image super-resolution. In this manner, Track 1 focuses on the problem of super-resolving the spatial resolution of spectral images given training pairs of low and high spatial resolution spectral images. Track 2, on the other hand, aims to leverage the inherently higher spatial resolution of colour (RGB) cameras and the link between spectral and trichromatic images of the scene. The challenge in both tracks is then to recover a super-resolved image making use of low-resolution imagery at the input. We also elaborate upon the methods used by the participants, summarise the results and discuss their rankings.
AB - In this paper, we describe the Perceptual Image Restoration and Manipulation (PIRM) workshop challenge on spectral image super-resolution, motivate its structure and conclude on results obtained by the participants. The challenge is one of the first of its kind, aiming at leveraging modern machine learning techniques to achieve spectral image super-resolution. It comprises of two tracks. The first of these (Track 1) is about example-based single spectral image super-resolution. The second one (Track 2) is on colour-guided spectral image super-resolution. In this manner, Track 1 focuses on the problem of super-resolving the spatial resolution of spectral images given training pairs of low and high spatial resolution spectral images. Track 2, on the other hand, aims to leverage the inherently higher spatial resolution of colour (RGB) cameras and the link between spectral and trichromatic images of the scene. The challenge in both tracks is then to recover a super-resolved image making use of low-resolution imagery at the input. We also elaborate upon the methods used by the participants, summarise the results and discuss their rankings.
KW - Hyperspectral
KW - Multispectral
KW - RGB
KW - Stereo
KW - Super-resolution
UR - http://www.scopus.com/inward/record.url?scp=85061716392&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-11021-5_22
DO - 10.1007/978-3-030-11021-5_22
M3 - Conference contribution
AN - SCOPUS:85061716392
SN - 9783030110208
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 356
EP - 371
BT - Computer Vision – ECCV 2018 Workshops, Proceedings
A2 - Leal-Taixé, Laura
A2 - Roth, Stefan
PB - Springer Verlag
Y2 - 8 September 2018 through 14 September 2018
ER -