A novel iterative shape from focus algorithm based on combinatorial optimization

Shim, Seong-O; Choi, Tae‐Sun

doi:10.1016/j.patcog.2010.05.029

Cited by 38 publications

(19 citation statements)

References 17 publications

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“…Unfortunately this may also cause smoothing of important image structures such that the resulting images appear blurred and not sharp everywhere. Hence, researchers came up with the idea of not applying the smoothness constraint on the resulting image itself, but on the per-pixel decision of the in-focus areas: In [13,14,15,16,17,18] the authors determine an initial decision map by means of a specific sharpness criterion. Subsequently they segment these maps into regions that belong to the same input frames.…”

Section: Related Workmentioning

confidence: 99%

“…There are many other approaches that offer improvements to focus fusion techniques and algorithms: Muhammad and Choi [20] derive the optimal sampling to obtain a reasonable 3-D shape. In [16], Shim and Choi introduce a novel iterative algorithm to reconstruct the 3-D shape. Mahmood et al [17] propose a combination of different focus measures for constructing the optimal decision map through genetic programming.…”

Section: Related Workmentioning

confidence: 99%

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A focus fusion framework with anisotropic depth map smoothing

Boshtayeva

Hafner

Weickert

2015

Pattern Recognition

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A focus fusion framework with anisotropic depth map smoothing

Boshtayeva

Hafner

Weickert

2015

Pattern Recognition

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“…To evaluate the sharpness of the acquired image, a variety of focus measures have been proposed in applications such as autofocus [15], depth from focus [16], and multifocus image fusion [17]. As an evaluation metric, focus measure is required to be unimodal, monotonic with respect to blur, and computationally effective.…”

Section: Introductionmentioning

confidence: 99%

“…As implied by their names, TEN is computed from image gradients, while GLV and SML are based on variance and the Laplacian, respectively. For more details, readers are referred to [16].…”

Section: Introductionmentioning

confidence: 99%

Autofocus for multispectral camera using focus symmetry

et al. 2012

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A multispectral camera acquires spectral color images with high fidelity by splitting the light spectrum into more than three bands. Because of the shift of focal length with wavelength, the focus of each channel should be mechanically adjusted in order to obtain sharp images. Because progressive adjustment is quite time consuming, the clear focus must be determined by using a limited number of images. This paper exploits the symmetry of focus measure distribution and proposes a simple yet efficient autofocus method. The focus measures are computed using first-order image derivatives, and the focus curve is obtained by spline interpolation. The optimal focus position, which maximizes the symmetry of the focus measure distribution, is then computed according to distance metrics. The effectiveness of the proposed method is validated in the multispectral camera system, and it is also applicable to relevant imaging systems.

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“…To determine the sharpness of a pixel, a focus measure is applied on small neighborhood of a pixel. Among the variety of focus measures [3,4,5,6,7], gray level variance (GLV) [3], Tenenbaum gradient (TEN) [6], and sum of modified Laplacian (SML) [7] are widely used. Most of SFF techniques [7,8,9] sum the focus values of neighboring pixels while computing the final focus value of a center pixel to suppress the noise effect.…”

Section: Introductionmentioning

confidence: 99%