Image despeckling with non-local total bounded variation regularization
| dc.contributor.author | Jidesh, P. | |
| dc.contributor.author | Banothu, B. | |
| dc.date.accessioned | 2020-03-31T08:35:17Z | |
| dc.date.available | 2020-03-31T08:35:17Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | A non-local total bounded variational (TBV) regularization model is proposed for restoring images corrupted with data-correlated speckles and linear blurring artifacts. The energy functional of the model is derived using maximum a posteriori (MAP) estimate of the noise probability density function (PDF). The non-local total bounded variation prior regularizes the model while the data fidelity is derived using the MAP estimator of the noise PDF. The computational efficiency of the model is improved using a fast numerical scheme based on the Augmented Lagrange formulation. The proposed model is employed to restore ultrasound (US) and synthetic aperture radar (SAR) images, which are usually speckled and blurred. The numerical results are presented and compared. Furthermore, a detailed theoretical study of the model is performed in addition to the experimental analysis. 2017 Elsevier Ltd | en_US |
| dc.identifier.citation | Computers and Electrical Engineering, 2018, Vol.70, , pp.631-646 | en_US |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/11523 | |
| dc.title | Image despeckling with non-local total bounded variation regularization | en_US |
| dc.type | Article | en_US |