A New Alternating Minimization Algorithm for Total Variation Image Reconstruction

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dc.contributor.authorWang, Yilun
dc.contributor.authorYang, Junfeng
dc.contributor.authorYin, Wotao
dc.contributor.authorZhang, Yin
dc.date.accessioned2018-06-18T17:58:14Z
dc.date.available2018-06-18T17:58:14Z
dc.date.issued2007-06
dc.date.noteJune 2007 (Revised on May 2008)
dc.description.abstractWe propose, analyze and test an alternating minimization algorithm for recovering images from blurry and noisy observa- tions with total variation (TV) regularization. This algorithm arises from a new half-quadratic model applicable to not only the anisotropic but also isotropic forms of total variation discretizations. The per-iteration computational complexity of the algorithm is three Fast Fourier Transforms (FFTs). We establish strong convergence properties for the algorithm including finite convergence for some variables and relatively fast exponential (or q-linear in optimization terminology) convergence for the others. Furthermore, we propose a continuation scheme to accelerate the practical convergence of the algorithm. Extensive numerical results show that our algorithm performs favorably in comparison to several state-of-the-art algorithms. In particular, it runs orders of magnitude faster than the Lagged Diffusivity algorithm for total-variation-based deblurring. Some extensions of our algorithm are also discussed.
dc.format.extent24 pp
dc.identifier.citationWang, Yilun, Yang, Junfeng, Yin, Wotao, et al.. "A New Alternating Minimization Algorithm for Total Variation Image Reconstruction." (2007) <a href="https://hdl.handle.net/1911/102074">https://hdl.handle.net/1911/102074</a>.
dc.identifier.digitalTR07-10
dc.identifier.urihttps://hdl.handle.net/1911/102074
dc.language.isoeng
dc.titleA New Alternating Minimization Algorithm for Total Variation Image Reconstruction
dc.typeTechnical report
dc.type.dcmiText
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