Name
Abstract | ||
|---|---|---|
This paper presents a new approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We do not assume any prior information about the shape of degradation blurs. The proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains. The image regularization based on variational principles maintains stable performance under severe noise corruption. The blur regularization guarantees consistency of the solution by exploiting differences among the acquired low-resolution images. Several experiments on synthetic and real data illustrate the robustness and utilization of the proposed technique in real applications. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TIP.2007.903256 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
deconvolution,image restoration,variational techniques,blur regularization,image regularization,multichannel blind deconvolution,multiple degraded low-resolution frames,regularized energy function,superresolution blind deconvolution,superresolution problem,variational principles,Image restoration,multichannel blind deconvolution,regularized energy minimization,resolution enhancement,superresolution | Computer vision,Blind deconvolution,Variational principle,Deconvolution,Image processing,Robustness (computer science),Regularization (mathematics),Artificial intelligence,Image restoration,Superresolution,Mathematics | Journal |
Volume | Issue | ISSN |
16 | 9 | 1057-7149 |
Citations | PageRank | References |
48 | 1.77 | 30 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filip Sroubek | 1 | 149 | 7.80 |
| G. Cristobal | 2 | 96 | 6.50 |
| J. Flusser | 3 | 398 | 25.42 |