Progressive Deep Network with Channel Back-Projection for Hyperspectral Recovery from RGB

Sang Ho Lee, Min Je Park, Jong Ok Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hyperspectral images are useful in a variety of fields such as remote sensing, medical diagnosis, and agriculture. But it requires very expensive professional equipment and a lot of time to obtain. In this paper, we propose a deep learning architecture that reconstructs hyperspectral images from RGB images that are easy to acquire in real time. Hyperspectral reconstruction is inherently difficult because much information is lost when hyperspectral bands are integrated into three RGB channels. To effectively overcome the problem of hyperspectral restoration, we design a neural network with the following three basic principles. First, it adopts a method in which channels are gradually increased through several steps to restore hyperspectral images. Second, it is learned on a group basis for efficient restoration. Hyperspectral bands are divided into three groups: R, G, and B. Finally, the concept of channel back projection is newly proposed. In the process of gradually performing hyperspectral reconstruction, the reconstructed image is refined by repeatedly projecting the reconstructed hyperspectral to RGB. In the experimental results, these three principles proved the performance that exceeds the state-of-theart methods.

Original languageEnglish
Title of host publication2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1257-1261
Number of pages5
ISBN (Electronic)9789881476883
Publication statusPublished - 2020 Dec 7
Event2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
Duration: 2020 Dec 72020 Dec 10

Publication series

Name2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
CountryNew Zealand
CityVirtual, Auckland
Period20/12/720/12/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Instrumentation

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