Xin Lin

Xin Lin

Coupled Dense Convolutional Neural Networks with Autoencoder for Unsupervised Hyperspectral Super-Resolution

Published:

Abstract

This paper proposes CoDenNet (Coupled Dense Convolutional Network), an unsupervised deep learning approach for hyperspectral image super-resolution reconstruction. By coupling dense convolutional layers with an autoencoder architecture, our method achieves superior performance in reconstructing high-resolution hyperspectral images from low-resolution inputs.

Key Contributions

  • Developed an unsupervised learning framework for hyperspectral super-resolution
  • Integrated dense convolutional networks with autoencoder for feature extraction
  • Achieved state-of-the-art performance on benchmark datasets
  • Presented at ICIG 2023 conference

Keywords

Hyperspectral imaging, Super-resolution, Deep learning, Convolutional neural networks, Unsupervised learning

Recommended citation: Lin, X. et al. (2023). Coupled Dense Convolutional Neural Networks with Autoencoder for Unsupervised Hyperspectral Super-Resolution. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14359. Springer, Cham. https://doi.org/10.1007/978-3-031-46317-4_14
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