Autofocusing based on power-spectra analysis

Jutamulia, Suganda; Asakura, Toshimitsu; Bahuguna, R. D.; Guzman, Panfilo C. De

doi:10.1364/ao.33.006210

Cited by 20 publications

(8 citation statements)

References 2 publications

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“…Such methods are usually based on the calculation of a "sharpness function" (SF), which is a real-valued estimate of the image focus. Commonly used sharpness functions in literature have been based on image derivatives [45,6,32,12,47,40], statistics [13,20,46,35,39] and Fourier transforms [24,44].…”

Section: Review Of Passive Image Autofocusmentioning

confidence: 99%

The Weibull manifold in low-level image processing: An application to automatic image focusing

Zografos

Lenz

Felsberg

2013

Image and Vision Computing

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In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, differencebased filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This maps each image to the 2D Weibull manifold. Third, we exploit the information geometry of this manifold and solve lowlevel image processing tasks as minimisation problems on point sets. For a proof-of-concept example, we examine the image autofocusing task. We propose appropriate cost functions together with a simple implicitly-constrained manifold optimisation algorithm and show that our framework compares very favourably against common autofocus methods from literature. In particular, our approach exhibits the best overall performance in terms of combined speed and accuracy.

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Section: Review Of Passive Image Autofocusmentioning

confidence: 99%

The Weibull manifold in low-level image processing: An application to automatic image focusing

Zografos

Lenz

Felsberg

2013

Image and Vision Computing

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“…The image Fourier transform is important for image quality improvement in electron microscopy, as well as in other types of optical devices, such as telescopes, ophthalmoscopes and endoscopes [8]. To this end, one needs on one hand a thorough analysis of the Fourier transform, on the other hand the analysis must be fast.…”

Section: Introductionmentioning

confidence: 99%

Orientation identification of the power spectrum

et al. 2011

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The image Fourier transform is widely used for defocus and astigmatism correction in electron microscopy. The shape of a power spectrum (the square of a modulus of image Fourier transform) is directly related to the three microscope's controls, namely defocus and two-fold (two-parameter) astigmatism. In this paper the new method for power spectrum orientation identification is proposed. The method is based on the three measures which are related to the microscope's controls. The measures are derived from the mathematical moments of the power spectrum. The method is tested with the help of a Gaussian benchmark, as well as with the scanning electron microscopy experimental images. The method can be used as an assisting tool for increasing the capabilities of defocus and astigmatism correction a of non-experienced scanning electron microscopy user, as well as a basis for automated application.

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“…The very first step in auto-focusing requires the correct judgment of whether an image is focused or not, and related research is the subject of much of the passive auto-focusing literature. [1][2][3][4][5][6][7] A number of different focus measures have been developed as indicators of focusing status; a focus measure is a function of digital images that gives a single value for each input image. The basic requirements of an acceptable focus measure are: (1) it is unimodal when evaluated over a wide range of defocus and (2) it is monotonic on each side of its peak value.…”

Section: Introductionmentioning

confidence: 99%

Do focus measures apply to retinal images?

2007

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The diverse needs for digital auto-focusing systems have driven the development of a variety of focus measures. The purpose of the current study was to investigate whether any of these focus measures are biologically plausible; specifically whether they are applicable to retinal images from which defocus information is extracted in the operation of accommodation and emmetropization, two ocular auto-focusing mechanisms. Ten representative focus measures were chosen for analysis, 6 in the spatial domain and 4 transform-based. Their performance was examined for combinations of non-defocus aberrations and positive and negative defocus. For each combination, a wavefront was reconstructed, the corresponding point spread function (PSF) computed using Fast Fourier Transform (FFT), and then the blurred image obtained as the convolution of the PSF and a perfect image. For each blurred image, a focus measure curve was derived for each focus measure. Aberration data were either collected from 22 real eyes or randomly generated data based on Gaussian parameters describing data from a published large scale human study (n>100). For the latter data set, analyses made use of distributed computing on a small inhomogeneous computer cluster. In the presence of small amounts of nondefocus aberrations, all focus measures showed monotonic changes with positive or negative defocus, and their curves generally remained unimodal, although there were large differences in their variability, sensitivity to defocus and effective ranges. However, the performance of a number of these focus measures became unacceptable when nondefocus aberrations exceed a certain level.

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Autofocusing based on power-spectra analysis

Abstract: An autofocusing mechanism based on the concept of autocorrelation is described. The method may be performed optically with ring detectors in the Fourier domain or electronically with a special computing chip or software.

Cited by 20 publications

References 2 publications

The Weibull manifold in low-level image processing: An application to automatic image focusing

The Weibull manifold in low-level image processing: An application to automatic image focusing

Orientation identification of the power spectrum

Do focus measures apply to retinal images?

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