Feasible and Robust Optimization Framework for Auxiliary Information Refinement in Spatially-Varying Image Enhancement
In content-based image processing, the precise inference of auxiliary information dominates various image enhancement applications. Given the rough auxiliary information provided by users or inference algorithms, a common scenario is to refine it with respect to the image content. Quadratic Laplacia...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 26(2017), 8 vom: 01. Aug., Seite 3721-3733 |
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| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2017
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article |
| Résumé: | In content-based image processing, the precise inference of auxiliary information dominates various image enhancement applications. Given the rough auxiliary information provided by users or inference algorithms, a common scenario is to refine it with respect to the image content. Quadratic Laplacian regularization is generally used as the refinement framework because of the availability of closed-form solutions. However, solving the resultant large linear system imposes a great burden on commodity computing hardware systems in the form of computational time and memory consumption, so efficient computing algorithms without losing precision are required, especially for large images. In this paper, we first analyze the geometric nature of the quadratic Laplacian regularization associated with the algebraic property of the corresponding linear system, which clarifies the essential issues causing ineffective solutions for conventional optimization algorithms. Correspondingly, we propose an optimization scheme that is capable of approaching the closed-form solution in an efficient manner using existing fast local filters, and we perform a spectral analysis to validate the robustness of this method in severe conditions. Finally, experimental results show that the proposed scheme is more feasible for large input images and is more robust to obtain the effective refinement than conventional algorithms |
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| Description: | Date Completed 30.07.2018 Date Revised 30.07.2018 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2017.2698922 |