2014
DOI: 10.1364/ao.53.002297
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Total variation regularization cost function for demodulating phase discontinuities

Abstract: We introduce a method based on the minimization of a total variation regularization cost function for computing discontinuous phase maps from fringe patterns. The performance of the method is demonstrated by numerical experiments with both synthetic and real data.

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Cited by 14 publications
(29 citation statements)
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“…For our application, it hence allows us to recover rapid changes in the refractive index profile that are not possible using the previous approaches. A total variation regularization technique for the recovery of phase functions has recently been reported [17]. However, this is applicable to the case where the phase function itself is expected to have discontinuities or rapid changes in them.…”
Section: Introductionmentioning
confidence: 99%
“…For our application, it hence allows us to recover rapid changes in the refractive index profile that are not possible using the previous approaches. A total variation regularization technique for the recovery of phase functions has recently been reported [17]. However, this is applicable to the case where the phase function itself is expected to have discontinuities or rapid changes in them.…”
Section: Introductionmentioning
confidence: 99%
“…As remarked in , this model allows the recovering of sharp phase transitions, something that other methods such as those based on L 2 regularization, fail to deliver. The model also allows to recover, at the same time, the background illumination a and the amplitude modulation b .…”
Section: Total Variation Based Modelmentioning
confidence: 98%
“…In this work, we are interested in the model presented in , where a variational method with total variation (TV) regularization to all three unknowns ϕ , a and b as shown below was presented. normalargnormalmina,b,ϕ1emitalicTV()a,b,ϕ,g{|ΩIg2dΩ+1λaΩadΩ10em}|+1λbΩbdΩ+1λϕΩ||ϕdΩ, where Ω ⊆ ℝ 2 is the domain of integration, g is a given fringe pattern, obeying the model described in (1), and λ a , λ b , λ ϕ are positive regularization parameters.…”
Section: Total Variation Based Modelmentioning
confidence: 99%
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