Abstract
We consider compressive imaging problems, where images are reconstructed from a reduced number of linear measurements. Our objective is to improve over current state of the art compressive imaging algorithms in terms of both reconstruction error and runtime. To pursue our objective, we propose a compressive imaging algorithm that employs the approximate message passing (AMP) framework. AMP is an iterative signal reconstruction algorithm that performs scalar denoising of noisy signals. In this work, we apply an adaptive Wiener filter, which is a wavelet-based image denoiser, within AMP. Numerical results show that the proposed algorithm improves over the state of the art in both reconstruction error and runtime.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
