DeepCASD: An End-to-End Approach for Multi-Spectural Image Super-Resolution


Multi-spectral (MS) image super-resolution aims to reconstruct super-resolved multi-channel images from their low-resolution images by regularizing the image to be reconstructed. Recently datadriven regularization techniques based on sparse modeling and deep learning have achieved substantial improvements in single image reconstruction problems. Inspired by these data-driven methods, we develop a novel coupled analysis and synthesis dictionary (CASD) model for MS image super-resolution, by exploiting a regularizer that operates within, as well as across, multiple spectral channels using convolutional dictionaries. To learn the CASD model parameters, we propose a deep dictionary learning framework, named DeepCASD, by unfolding and training an end-to-end CASD based reconstruction network over an image data set. Experimental results show that the DeepCASD framework exhibits improved performance on multi-spectral image super-resolution compared to state-of-the-art learning based super-resolution algorithms.


  • Related News & Events

    •  NEWS    MERL presenting 9 papers at ICASSP 2018
      Date: April 15, 2018 - April 20, 2018
      Where: Calgary, AB
      MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Philip V. Orlik; Pu (Perry) Wang
      Research Areas: Computational Sensing, Digital Video, Speech & Audio
      • MERL researchers are presenting 9 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Calgary from April 15-20, 2018. Topics to be presented include recent advances in speech recognition, audio processing, and computational sensing. MERL is also a sponsor of the conference.

        ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.