A New Alternating Minimization Algorithm for Total Variation Image Reconstruction

Simple item page
dc.contributor.authorWang, Yilunen_US
dc.contributor.authorYang, Junfengen_US
dc.contributor.authorYin, Wotaoen_US
dc.contributor.authorZhang, Yinen_US
dc.date.accessioned2018-06-18T17:58:14Zen_US
dc.date.available2018-06-18T17:58:14Zen_US
dc.date.issued2007-06en_US
dc.date.noteJune 2007 (Revised on May 2008)en_US
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.en_US
dc.format.extent24 ppen_US
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>.en_US
dc.identifier.digitalTR07-10en_US
dc.identifier.urihttps://hdl.handle.net/1911/102074en_US
dc.language.isoengen_US
dc.titleA New Alternating Minimization Algorithm for Total Variation Image Reconstructionen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
TR07-10.pdf
Size:
369.06 KB
Format:
Adobe Portable Document Format
Download
Collections
CMOR Technical Reports