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Publication - Professor David Bull

    Compressive imaging using approximate message passing and a Cauchy prior in the wavelet domain

    Citation

    Hill, PR, Kim, J-H, Basarab, A, Kouamé, D, Bull, D & Achim, AM, 2017, ‘Compressive imaging using approximate message passing and a Cauchy prior in the wavelet domain’. in: 2016 IEEE International Conference on Image Processing (ICIP 2016). Institute of Electrical and Electronics Engineers (IEEE), pp. 2514-2518

    Abstract

    Approximate Message Passing (AMP) is an iterative reconstruction algorithm that performs signal denoising within a compressive sensing framework. We propose the use of heavy tailed distribution based image denoising, specifically using a Cauchy prior based Maximum A-Posteriori (MAP) estimate within a wavelet based AMP compressive sensing structure. The use of this MAP denoising algorithm provides extremely fast convergence for image based compressive sensing. The proposed method converges approximately twice as fast as the compared AMP methods whilst providing superior final MSE results over a range of measurement rates.

    Full details in the University publications repository