Terahertz Imaging of Binary Reflectance with Variational Bayesian Inference


In this paper, we propose a Bayesian inference approach to extract the binary reflectance pattern of samples from compressed measurements in the terahertz (THz) frequency band. Compared with existing compressed THz imaging methods relying on the sparsity of the reflectance pattern, the proposed Bayesian approach exploits the non-negative binary nature of the reflectance without any assumption on its spatial pattern information and enables a pixel-wise iterative inference approach for fast signal recovery. Numerical evaluation confirms the effectiveness of the proposed approach.


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    •  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.