Name
Abstract | ||
|---|---|---|
In this paper, we present a new general method for image fusion based on Partial Differential Equation (PDE).We propose to combine pixel-level fusion and diffusion processes through one single powerful equation. The insertion of the relevant information contained in sources is achieved in the fused image by reversing the diffusion process. To solve the well-known instability problem of an inverse diffusion process, a regularization term is added. One of the advantages of such an original approach is to improve the quality of the results in case of noisy input images. Finally, few examples and comparisons with classical fusion models will demonstrate the efficiency of our method both on blurred and noisy images. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74607-2_11 | ACIVS |
Keywords | Field | DocType |
image fusion,diffusion process,partial differential equation | Anisotropic diffusion,Diffusion process,Inverse,Image fusion,Pattern recognition,Computer science,Reversing,Fusion,Regularization (mathematics),Artificial intelligence,Partial differential equation | Conference |
Volume | ISSN | ISBN |
4678 | 0302-9743 | 3-540-74606-4 |
Citations | PageRank | References |
4 | 0.42 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sorin Pop | 1 | 27 | 3.02 |
| Olivier Lavialle | 2 | 72 | 9.51 |
| Romulus Terebes | 3 | 49 | 8.42 |
| Monica Borda | 4 | 63 | 13.54 |