Phase retrieval from single frame projection fringe pattern with variational image decomposition

Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement

“…where a(x, y) is the background, b(x, y) and φ(x, y) are the modulation intensity and the optical phase, f 0 is carrier frequency, and noise denotes the noise in I(x, y). Phase retrieval can be implemented by extracting the fringe part b(x, y) cos(φ(x, y) + 2πf 0 x) apart from the background a(x, y) and noise part [13]. However, due to the discontinuous edge of objects and noise effect, the fringe part and other parts are not well separated.…”

“…However, phase retrieval from single frame fringe pattern is a challenging problem in fringe projection 3D measurement especially for objects with edges or abrupt changes in depth, which attracts wide attention. Numerous methods have been proposed, such as the well-known Fourier Transform method (FT), Windowed Fourier Transform method (WFT), the Wavelet Transform method (WT), Shearlet Transform method (ST), and the more effective methods such as Empirical Mode Decomposition (EMD) method and more recently proposed variational image decomposition(VID) and variational mode decomposition (VMD) methods [8][9][10][11][12][13][14][15]. Although #372727 https://doi.org/10.1364/OE.27.028929 extensive research efforts have been made for phase retrieval, it is hard to implement an accurate and fast retrieval phase method for the tradeoff between the accuracy and computational efficiency in traditional phase retrieval methods.…”

A Mean Curvature Regularized Based Model for Demodulating Phase Maps from Fringe Patterns

“…(1.1) to recover the three unknowns a, b and φ, plus the nonlinearity of the cosine function makes the problem very challenging. Recently, variational techniques that aim to reduce uncertainty of the solution by introducing more information into the model by means of regularization of the unknown variables have proved to deliver a feasible solution to this problem, see [10,17,30] and references therein. The new information introduced in the form of a regularizer defines the properties of the variational solution so a careful selection is advised.…”

Variational image decomposition for estimation of fringe orientation and density from electronic speckle pattern interferometry fringe patterns with greatly variable density

“…In [11], Rudin et al have proposed the earliest Rudin-Osher-Fatemi image decomposition model, which split an image f into two components = + f u v, such that u represents a cartoon or geometric component of f, while v represents the oscillatory component of f. In [12], Meyer has proposed the -TV G model, which use the total variation (TV) of an image to model u and the norm G to model v. Also, other decomposition models such as -− TV H 1 model [13], -TV Hilbert model [14] and so on were proposed. Particularly, in [15][16][17], Zhu et al have proposed the variational image decomposition for optical fringe filtering and automatic background removing.…”