Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/9577
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dc.contributor.authorJidesh, P.
dc.date.accessioned2020-03-31T06:51:10Z-
dc.date.available2020-03-31T06:51:10Z-
dc.date.issued2014
dc.identifier.citationComputers and Electrical Engineering, 2014, Vol.40, 8, pp.66-78en_US
dc.identifier.uri10.1016/j.compeleceng.2014.03.013
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/9577-
dc.description.abstractMany variational formulations are introduced over the last few years to handle multiplicative data-dependent noise. Some of these models seek to minimize the Total Variation (TV) norm of the absolute gradient function subject to given constraints. Since the TV-norm (well-defined in the space of bounded variations (BVs)) minimization eventually results in the formation of piece-wise constant patches during the evolution process, the filtered output appears blocky. In this work the block effect (commonly known as staircase effect) is being handled by using a convex combination of TV and Tikhonov filters, which are defined in BV and L2 (square-integrable functions) spaces, respectively. The constraint for the minimizing functional is derived based on a maximum a posteriori (MAP) regularization approach, duly considering the noise distributions. Therefore, this model is capable of denoising speckled images, whose intensity is Gamma distributed. The results are demonstrated both in terms of visual and quantitative measures. 2014 Elsevier Ltd. All rights reserved.en_US
dc.titleA convex regularization model for image restorationen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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