Name
Abstract | ||
|---|---|---|
In this work, an iterative inversion algorithm for deblurring and deconvolution is considered. The algorithm is based on the conjugate gradient scheme and uses the so-called weighted L2-norm regularizer to obtain a reliable solution. The regularizer is included as a multiplicative constraint. In this way, the appropriate regularization parameter will be controlled by the optimization process itself. In fact, the misfit in the error in the space of the blurring operator is the regularization parameter. Then, no a priori knowledge on the blurred data or image is needed. If noise is present, the misfit in the error consisting of the blurring operator will remain at a large value during the optimization process; therefore, the weight of the regularization factor will be more significant. Hence, the noise will, at all times, be suppressed in the reconstruction process. Although one may argue that, by including the regularization factor as a multiplicative constraint, the linearity of the problem has been lost, careful analysis shows that, under certain restrictions, no new local minima are introduced. Numerical testing shows that the proposed algorithm works effectively and efficiently in various practical applications. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TIP.2004.836172 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
optimisation,noise suppression,deblurring problem,optimization process,conjugate gradient scheme,multiplicative constraint,iterative inversion algorithm,regularization factor,l2-norm regularizer,image denoising,inverse problems,deconvolution,deblurring problems,image reconstruction,optimization,proposed algorithm,reconstruction process,appropriate regularization parameter,multiplicative regularization approach,weighted l/sub 2/-norm regularizer,conjugate gradient methods,iterative methods,careful analysis,regularization parameter,regularization,indexing terms,linearity,ill posed,image restoration,algorithm,inverse problem,iterative method,a priori knowledge,conjugate gradient,conjugate gradient method,image processing,local minima | Conjugate gradient method,Deconvolution,Regularization (mathematics),Inverse problem,Artificial intelligence,Image restoration,Mathematical optimization,Deblurring,Pattern recognition,Iterative method,Algorithm,Mathematics,Regularization perspectives on support vector machines | Journal |
Volume | Issue | ISSN |
13 | 11 | 1057-7149 |
Citations | PageRank | References |
6 | 0.70 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Abubakar | 1 | 19 | 2.53 |
| Peter M van den Berg | 2 | 6 | 0.70 |
| T. M. Habashy | 3 | 12 | 2.17 |
| Henning Braunisch | 4 | 39 | 4.47 |