Name
Abstract | ||
|---|---|---|
We describe a wavelet-based approach to linear inverse problems in image processing. In this approach, both the images and the linear operator to be inverted are represented by wavelet expansions, leading to a multiresolution sparse matrix representation of the inverse problem. The constraints for a regularized solution are enforced through wavelet expansion coefficients. A unique feature of the wavelet approach is a general and consistent scheme for representing an operator in different resolutions, an important problem in multigrid/multiresolution processing. This and the sparseness of the representation induce a multigrid algorithm. The proposed approach was tested on image restoration problems and produced good results. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/83.382493 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
linear operator,wavelet expansion coefficient,image representation,linear inverse problem,image processing,wavelet transforms,multigrid algorithm,image resolution,wavelet coefficient,matrix algebra,image restoration,inverse problems,multigrid/multiresolution processing,different resolution,problem solving,wavelet-based approach,wavelet approach,inverse problem,constraints,image restoration problem,wavelet expansion coefficients,multiresolution processing,important problem,multiresolution sparse matrix representation,regularized solution,wavelet expansion,linear inverse problems,image segmentation,frequency,multiresolution analysis,lattices,image reconstruction,motion estimation,iterative methods | Mathematical optimization,Pattern recognition,Computer science,Multiresolution analysis,Image processing,Artificial intelligence,Inverse problem,Image restoration,Wavelet packet decomposition,Multigrid method,Wavelet transform,Wavelet | Journal |
Volume | Issue | ISSN |
4 | 5 | 1057-7149 |
Citations | PageRank | References |
21 | 2.86 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaofeng Wang | 1 | 24 | 10.09 |
| jun zhang | 2 | 177 | 26.49 |
| guangwen pan | 3 | 21 | 2.86 |