Browsing by Author "Jesus, Bruno"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Batch algorithms of matching pursuit and orthogonal matching pursuit with applications to compressed sensingPublication . Wang, Huiyuan; Vieira, José M. N.; Ferreira, Paulo J. S. G.; Jesus, Bruno; Duarte, IsabelBatch algorithms of matching pursuit (MP) and orthogonal matching pursuit (OMP) are proposed in this paper. In both algorithms, the original iteration procedures are modified in the following way. Instead of finding a single best-matched atom in each iteration, we find a number of best-matched atoms to speed up the convergence, - a batch version. Then optimized coefficients are computed based on these atoms. Numerical simulations in the application to compressed sensing show that the proposed algorithms are much faster than the original ones, while similar reconstruction precision is obtained.
- A Novel Memory-Efficient Fast Algorithm for 2-DPublication . Wang, Huiyuan; Vieira, José; Jesus, Bruno; Duarte, Isabel; Ferreira, PauloThe basic theories of compressed sensing (CS) turn around the sampling and reconstruction of 1-D signals. To deal with 2-D signals (images), the conventional treatment is to convert them into1-D vectors. This has drawbacks, including huge memory demands and difficulties in the design and calibration of the optical imaging systems. As a result, in 2009 some researchers proposed the concept of compressed imaging (CI) with separable sensing operators. However, their work is only focused on the sampling phase. In this paper, we propose a scheme for 2-D CS that is memory- and computation-efficient in both sampling and reconstruction. This is achieved by decomposing the 2-D CS problem into two stages with the help of an intermediate image. The intermediate image is then solved by direct orthogonal linear transform and the original image is reconstructed by solving a set of 1-D l1-norm minimization sub-problems. The experimental results confirm the feasibility of the proposed scheme.