Compressive Imaging via Approximate Message Passing
Abstract
This project considers compressive imaging problems, where images are reconstructed from as few linear measurements as possible. Compressive imaging can be applied to a broad range of applications, including medical imaging, seismic imaging, and hyperspectral imaging. We propose novel compressive imaging algorithms that employ approximate message passing (AMP), which is an iterative signal estimation algorithm that performs component-wise denoising to noisy signals. In contrast, we apply non-separable denoisers to imaging with random matrices and hyperspectral imaging in CASSI systems. Numerical results demonstrate that our proposed algorithms significantly improve over current state-of-the-art compressive imaging algorithms in terms of both estimation error and run-time.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 04, 2015
- Accession Number
- AD1001362
Entities
People
- Dror Baron
Organizations
- North Carolina State University